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Tag: doc

1363 items with this tag.

Nov 09, 2026
Global Sensitivity Analysis - Overview
Nov 09, 2026
Local vs Global Sensitivity Analysis
Nov 09, 2026
Morris Elementary Effects Screening
Nov 09, 2026
Sampling and Estimation for Sobol Indices
Nov 09, 2026
Variance-Based Sensitivity and Sobol Indices
Nov 09, 2026
Generative Agents Architecture - Memory, Reflection and Planning
Nov 09, 2026
Homo Silicus - LLMs as Simulated Economic Agents
Nov 09, 2026
LLM Agents vs Rule-Based Agents in ABM
Nov 09, 2026
LLM-Powered Agents - Overview
Nov 09, 2026
Opinion Alignment Metrics for Language Models
Nov 09, 2026
Persona Mixture Calibration of LLM Agents
Nov 09, 2026
Silicon Samples and Algorithmic Fidelity
Nov 09, 2026
Validity, Bias and Calibration of LLM-Simulated Populations
Nov 09, 2026
Amortized vs Sequential Inference
Nov 09, 2026
Benchmarking and Diagnosing SBI (SBC, Coverage, C2ST)
Nov 09, 2026
Neural Likelihood Estimation and Sequential Neural Likelihood
Nov 09, 2026
Neural Posterior Estimation (NPE)
Nov 09, 2026
Neural Ratio Estimation
Nov 09, 2026
Neural SBI for Agent-Based and Economic Models
Nov 09, 2026
Neural Simulation-Based Inference - Overview
Nov 09, 2026
Normalizing Flows as Conditional Density Estimators
Nov 09, 2026
Automatic Differentiation Variational Inference (ADVI)
Nov 09, 2026
Diagnosing Variational Inference (PSIS k-hat and VSBC)
Nov 09, 2026
Mean-Field Family and Coordinate Ascent VI (CAVI)
Nov 09, 2026
Normalizing Flows for Variational Inference
Nov 09, 2026
Reparameterization Trick and Variational Autoencoders
Nov 09, 2026
Stochastic and Black-Box Variational Inference
Nov 09, 2026
The ELBO and KL Divergence Minimization
Nov 09, 2026
Variational Inference - Overview
Nov 09, 2026
Empirical Bayes - Overview
Nov 09, 2026
Empirical Bayes Interpretation of Shrinkage
Nov 09, 2026
James-Stein Estimator
Nov 09, 2026
Robbins Formula and Poisson Empirical Bayes
Nov 09, 2026
Stein's Paradox and Risk Dominance
Nov 09, 2026
Asymptotic Normality and Inference for Forests
Nov 09, 2026
Causal Machine Learning - Overview
Nov 09, 2026
Cross-Fitting and Sample Splitting
Nov 09, 2026
DML Estimators for ATE and the Interactive Model
Nov 09, 2026
Generalized Random Forests - Local Moment Equations
Nov 09, 2026
Honest Trees and Causal Forests
Nov 09, 2026
Neyman Orthogonality
Nov 09, 2026
R-Learner and Orthogonal CATE Estimation
Nov 09, 2026
Regularization Bias and the Partially Linear Model
Nov 09, 2026
BLP Demand Estimation - Overview
Nov 09, 2026
GMM Estimation and Instruments for Price Endogeneity
Nov 09, 2026
Numerical Integration and Optimization in PyBLP
Nov 09, 2026
Random Coefficients Logit Model
Nov 09, 2026
Supply Side and Markups
Nov 09, 2026
The BLP Contraction Mapping
Nov 09, 2026
Fisher Randomization Test and the Sharp Null
Nov 09, 2026
Permutation Tests and Exact Inference
Nov 09, 2026
Randomization Inference - Overview
Nov 09, 2026
Sharp vs Weak Null Hypotheses
Nov 09, 2026
Studentized Randomization Tests
Nov 09, 2026
Common Support and Overlap
Nov 09, 2026
Covariate Balance Diagnostics
Nov 09, 2026
Matching Methods and Distance Measures
Nov 09, 2026
Propensity Score Matching - Overview
Nov 09, 2026
Propensity Score and the Balancing Property
Nov 09, 2026
Event Study Designs and Dynamic Treatment Effects
Nov 09, 2026
Honest DiD - Sensitivity to Parallel Trends Violations
Nov 09, 2026
Pre-Trend Testing and Its Pitfalls
Nov 09, 2026
SDID Estimator - Unit and Time Weights
Nov 09, 2026
SDID Inference - Bootstrap, Jackknife and Placebo
Nov 09, 2026
SDID for Geo Experiments and Marketing Panels
Nov 09, 2026
SDID vs DiD vs Synthetic Control
Nov 09, 2026
Synthetic Difference-in-Differences - Overview
Nov 09, 2026
Conformal Inference for Counterfactuals and ITEs
Nov 09, 2026
Conformal Prediction - Overview
Nov 09, 2026
Conformal Prediction Under Covariate Shift
Nov 09, 2026
Conformalized Quantile Regression
Nov 09, 2026
Conformity Scores and Adaptive Prediction Sets
Nov 09, 2026
Marginal vs Conditional Coverage
Nov 09, 2026
Split Conformal Prediction and the Coverage Guarantee
Nov 09, 2026
Chain-of-Thought Prompting
Nov 09, 2026
Evaluating LLM Systems - Benchmarks, Hallucination and Human Preference
Nov 09, 2026
LLM Reasoning, Retrieval and Agents - Overview
Nov 09, 2026
RLHF and Instruction Tuning
Nov 09, 2026
ReAct - Reasoning and Acting Agents
Nov 09, 2026
Retrieval-Augmented Generation (RAG)
Nov 09, 2026
Reward Modeling from Human Preferences
Nov 09, 2026
Tool Use and the Agent Loop
Nov 09, 2026
DeepAR and Global Autoregressive Neural Forecasters
Nov 09, 2026
Forecast Evaluation and Backtesting
Nov 09, 2026
Hierarchical Forecast Reconciliation (MinT)
Nov 09, 2026
Local vs Global Forecasting Models
Nov 09, 2026
Probabilistic Forecasting - Overview
Nov 09, 2026
Proper Scoring Rules (CRPS, Log Score, Pinball Loss)
Nov 09, 2026
Time-Series Foundation Models (Chronos)
Nov 09, 2026
Autoregressive Language Modeling and Pretraining
Nov 09, 2026
Compute-Optimal Training (Chinchilla)
Nov 09, 2026
In-Context Learning and Few-Shot Prompting
Nov 09, 2026
Neural Scaling Laws
Nov 09, 2026
Scaled Dot-Product and Multi-Head Attention
Nov 09, 2026
Transformer Architecture and Positional Encoding
Nov 09, 2026
Transformers and LLM Foundations - Overview
Nov 09, 2026
Bayesian Media Mix Modeling - Overview
Nov 09, 2026
BG-NBD Model
Nov 09, 2026
Bayesian and Hierarchical Extensions of CLV Models
Nov 09, 2026
Customer Lifetime Value - Overview
Nov 09, 2026
Gamma-Gamma Model of Monetary Value
Nov 09, 2026
Pareto-NBD Model
Nov 09, 2026
RFM Sufficient Statistics and Iso-Value Curves
Nov 09, 2026
Shifted-Beta-Geometric Model for Contractual Retention
Nov 09, 2026
Conversion Lift Studies on Ad Platforms
Nov 09, 2026
Experimental Benchmarks for Observational Ad Measurement
Nov 09, 2026
From ITT to Treatment-on-the-Treated in Ad Experiments
Nov 09, 2026
Identity Fragmentation and the Privacy-Era Limits of User-Level Tests
Nov 09, 2026
Intent-to-Treat, PSA and Ghost Ad Designs
Nov 09, 2026
Predicted Ghost Ads and Ghost Bids Mechanics
Nov 09, 2026
The Unfavorable Economics of Ad Experiments - Power and Signal-to-Noise
Nov 09, 2026
User-Level Ad Experiments - Overview
Nov 09, 2026
User-Level vs Geo-Level Experiments - When to Use Which
Nov 09, 2026
Strobl 2007 - Bootstrap-Induced Association
Nov 09, 2026
Strobl 2007 - C-to-U RNA Editing Application
Nov 09, 2026
Strobl 2007 - Conclusions, Scope and Practical Guidance
Nov 09, 2026
Strobl 2007 - Null and Power Case Results
Nov 09, 2026
Strobl 2007 - Null and Power Case Simulation Design
Nov 09, 2026
Strobl 2007 - RF Importance Measures and the cforest Alternative
Nov 09, 2026
Strobl 2007 - Split-Selection Bias in CART Trees
Nov 09, 2026
Strobl et al 2007 - Bias in RF Variable Importance - Overview
Nov 09, 2026
Model Validation and Interpretability - Overview
Nov 09, 2026
Granger 2003 - Cointegration and the Error-Correction Representation
Nov 09, 2026
Granger 2003 - Econometrics, Integrated Series and the String-of-Pearls Picture
Nov 09, 2026
Granger 2003 - Forecasting, Decision Science and Aggregation
Nov 09, 2026
Granger 2003 - Granger Causality and Its Link to Cointegration
Nov 09, 2026
Granger 2003 - Spurious Regression Monte Carlo in Python
Nov 09, 2026
Granger 2003 - Spurious Regression and Cointegration - Overview
Nov 09, 2026
Granger 2003 - Spurious Regression from Yule to Granger-Newbold
Nov 09, 2026
Classic Linear Solvers - A Review
Nov 09, 2026
Computational Constraints on Probabilistic Solvers
Nov 09, 2026
Conjugate Gradients as Probabilistic Inference
Nov 09, 2026
Gaussian Priors over Matrices and the Symmetric Kronecker Product
Nov 09, 2026
Probabilistic Linear Solvers - Algorithmic Scaffold
Nov 09, 2026
The Linear Algebra Problem and Evaluation Strategies
Nov 09, 2026
Uncertainty Calibration for Linear Solvers
Nov 09, 2026
Always-Valid p-values and the mSPRT
Nov 09, 2026
CUPED and Regression-Adjusted Variance Reduction
Nov 09, 2026
Confidence Sequences
Nov 09, 2026
Interference and Marketplace Experiments
Nov 09, 2026
Online Experimentation - Overview
Nov 09, 2026
Sample Ratio Mismatch and Trustworthiness Checks
Nov 09, 2026
Switchback Experiment Design and Analysis
Nov 09, 2026
The Peeking Problem and Optional Stopping
Nov 09, 2026
Bronk Ramsey 2009 - Calibration Likelihood and the Calibration Curve
Nov 09, 2026
Girosi King 2007 - Lee-Carter Model Estimation and Forecasting
Nov 09, 2026
Girosi King 2007 - Universal Increasing Nonsmoothness of Age Profiles
Nov 09, 2026
Vickers 2019 - Net Benefit and Threshold Probability
Nov 09, 2026
Banbura 2013 - Mixed Frequency State Space and Mariano-Murasawa Aggregation
Nov 09, 2026
Gostic 2020 - Instantaneous vs Case Reproductive Number and the Three Estimators
Nov 09, 2026
Choosing the Global Scale and Effective Nonzeros
Nov 09, 2026
Allcott 2015 - Metadata Analysis of Site Selection Mechanisms
Nov 09, 2026
Allcott 2015 - Microdata Extrapolation and the Prediction Failure
Nov 09, 2026
Allcott 2015 - Model of External Validity and Site Selection Bias
Nov 09, 2026
Allcott 2015 - Opower Program, Selection Mechanisms and Data
Nov 09, 2026
Allcott 2015 - Site Selection Bias in Program Evaluation - Overview
Nov 09, 2026
Dahabreh 2020 - Identifiability Conditions and Identification
Nov 09, 2026
Dahabreh 2020 - Outcome Model, Inverse Odds Weighting and Doubly Robust Estimators
Nov 09, 2026
Dahabreh 2020 - Practical Issues, Diagnostics and Discussion
Nov 09, 2026
Dahabreh 2020 - Trial Participation Process, Designs and Estimands
Nov 09, 2026
Degtiar 2023 - Marketing Application Transporting Experiment Results
Nov 09, 2026
Dwork Roth 2014 - Report Noisy Max and the Exponential Mechanism
Nov 09, 2026
TBR Design Sensitivity and the Stationarity Assumption
Nov 09, 2026
Lewis Rao 2015 - Impact-to-SD Ratio Derivation and Calibrated Campaign
Nov 09, 2026
Blake 2015 - Consumer and Product Heterogeneity in Paid Search Response
Nov 09, 2026
Chan Perry 2017 - Selection Bias in MMM (Ad Targeting, Seasonality, Funnels, Endogenous Controls)
Nov 09, 2026
Morris et al 2019 - Applying ADEMP to Marketing-Model Validation
Nov 09, 2026
Morris et al 2019 - Protocols, Code Release, Consortia and Limits
Nov 09, 2026
AMSS Sales Event - Demand Curves, Competition and Post-Purchase Migration
Nov 09, 2026
ODE Filters and Smoothers
Nov 09, 2026
Perturbative ODE Solvers
Nov 09, 2026
Theory of ODE Filters and Smoothers
Nov 08, 2026
Betancourt 2015 - Euclidean HMC Weaknesses - Step Size and Density Variation
Nov 08, 2026
Betancourt 2015 - Gibbs, Random-Walk Metropolis and Reparameterisation
Nov 08, 2026
Betancourt 2015 - HMC for Hierarchical Models - Overview
Nov 08, 2026
Betancourt 2015 - Hierarchical Geometry and the Funnel
Nov 08, 2026
Betancourt 2015 - One-Way Normal Experiments and Stan Models
Nov 08, 2026
Betancourt 2015 - Riemannian HMC and the SoftAbs Metric
Nov 08, 2026
Bayesian HMMs - Label Switching, Non-Homogeneous Transitions and Dwell Times
Nov 08, 2026
Marginalising Discrete Latents - Why NUTS Cannot Sample Discrete Parameters
Nov 08, 2026
Stochastic Volatility and the Inference Frontier
Nov 08, 2026
Hoffman 2021 - ChEES Criterion and Algorithm 1
Nov 08, 2026
Hoffman 2021 - ChEES-HMC - Overview
Nov 08, 2026
Hoffman 2021 - Experiments - ESS per Gradient and GPU Throughput
Nov 08, 2026
Hoffman 2021 - Gaussian Autocorrelation Analysis and Trajectory Jitter
Nov 08, 2026
Hoffman 2021 - HMC Background and the GPU Problem for NUTS
Nov 08, 2026
Hoffman 2021 - Jitter Ablations, Discussion and Many-Chain Practice
Nov 08, 2026
Hoffman 2022 - Appendix - Hogwild Counterexample and Hessian Identity Proof
Nov 08, 2026
Hoffman 2022 - Ensemble-Chain Adaptation and Parallelism
Nov 08, 2026
Hoffman 2022 - GHMC and Neal Persistent Slice Sampling
Nov 08, 2026
Hoffman 2022 - MEADS - Experiments and Ablations
Nov 08, 2026
Hoffman 2022 - MEADS - Overview
Nov 08, 2026
Hoffman 2022 - MEADS Heuristics - Step Size, Damping, Drift and Eigenvalue Estimation
Nov 08, 2026
Rabiner 1989 - HMM Tutorial - Overview
Nov 08, 2026
Rabiner 1989 - HMM Types, Continuous Densities and State Duration
Nov 08, 2026
Rabiner 1989 - Implementation Issues - Scaling, Multiple Sequences and Initialisation
Nov 08, 2026
Rabiner 1989 - Markov Chains, HMM Definition and the Three Problems
Nov 08, 2026
Rabiner 1989 - Problem 1 - Forward and Backward Algorithms
Nov 08, 2026
Rabiner 1989 - Problem 2 - State Posteriors and the Viterbi Algorithm
Nov 08, 2026
Rabiner 1989 - Problem 3 - Baum-Welch Reestimation
Nov 08, 2026
Rabiner 1989 - Speech Recognition Applications and Results
Nov 08, 2026
Bronk Ramsey 2009 - Bayesian Analysis of Radiocarbon Dates - Overview
Nov 08, 2026
Bronk Ramsey 2009 - Deposition Models and Sequences
Nov 08, 2026
Bronk Ramsey 2009 - MCMC Outputs Diagnostics and Agreement Indices
Nov 08, 2026
Bronk Ramsey 2009 - Order Constraints and the Uniform Phase Prior
Nov 08, 2026
Bronk Ramsey 2009 - Other Group Priors Multiphase Models and Cross-Referencing
Nov 08, 2026
Concept Note - Abridged Life Tables Years of Life Lost and the Counterfactual Path
Nov 08, 2026
Concept Note - Forecasting Replication from Published Effects
Nov 08, 2026
Girosi King 2007 - Information Loss with One Principal Component
Nov 08, 2026
Girosi King 2007 - Lee-Carter as a Random Walk with Drift
Nov 08, 2026
Girosi King 2007 - Second-Stage Reestimation and Wilmoth Estimators
Nov 08, 2026
Girosi King 2007 - Understanding the Lee-Carter Method - Overview
Nov 08, 2026
Rizopoulos 2016 - Association Structures and Transformation Functions
Nov 08, 2026
Rizopoulos 2016 - Dynamic Predictions and Bayesian Model Averaging
Nov 08, 2026
Rizopoulos 2016 - Implementation MCMC and Baseline Hazard
Nov 08, 2026
Rizopoulos 2016 - JMbayes Joint Models Overview
Nov 08, 2026
Rizopoulos 2016 - Joint Model Specification and Posterior
Nov 08, 2026
Rizopoulos 2016 - PBC Basic Joint Model and Extended Longitudinal Outcomes
Nov 08, 2026
Rizopoulos 2016 - Predictive Accuracy and Cross-Validation
Nov 08, 2026
Simoiu 2017 - Benchmark and Outcome Tests and the Infra-Marginality Problem
Nov 08, 2026
Simoiu 2017 - Infra-Marginality and the Threshold Test - Overview
Nov 08, 2026
Simoiu 2017 - Limitations Extensions and Identification Ridge
Nov 08, 2026
Simoiu 2017 - Model Checks Robustness and Placebo Tests
Nov 08, 2026
Simoiu 2017 - North Carolina Traffic Stops and the Three Tests
Nov 08, 2026
Simoiu 2017 - Threshold Test Generative Model
Nov 08, 2026
Wiecki 2013 - Drift-Diffusion Model and Wiener First-Passage Likelihood
Nov 08, 2026
Wiecki 2013 - HDDM Hierarchical Drift-Diffusion Model - Overview
Nov 08, 2026
Wiecki 2013 - Hierarchical Bayesian Estimation Regression and Model Checking in HDDM
Nov 08, 2026
Wiecki 2013 - Parameter Recovery Simulations and Detection Power
Nov 08, 2026
Arora 2012 - Experiments and Evaluation Metrics
Nov 08, 2026
Arora 2012 - FastAnchorWords Algorithm and Guarantees
Nov 08, 2026
Arora 2012 - Practical Provable Topic Modeling - Overview
Nov 08, 2026
Arora 2012 - Recover Algorithms RecoverKL and RecoverL2
Nov 08, 2026
Arora 2012 - Separability and Anchor Words
Nov 08, 2026
Index: Arora 2012 - Practical Provable Topic Modeling
Nov 08, 2026
Blei 2003 - LDA Beyond Text Baskets and Sessions
Nov 08, 2026
Blei 2003 - LDA Generative Model and Exchangeability
Nov 08, 2026
Blei 2003 - Latent Dirichlet Allocation - Overview
Nov 08, 2026
Blei 2003 - Related Models and the Topic Simplex
Nov 08, 2026
Blei 2003 - Smoothed LDA, Examples and Experiments
Nov 08, 2026
Blei 2003 - Variational Inference and Parameter Estimation
Nov 08, 2026
Index: Blei 2003 - Latent Dirichlet Allocation
Nov 08, 2026
Concept - DP Mixtures for Image Segmentation
Nov 08, 2026
Concept - Label Switching and Label-Invariant Summaries
Nov 08, 2026
Concept - Neural and Embedding Topic Models
Nov 08, 2026
Concept - Short-Text Topic Models GSDMM and BTM
Nov 08, 2026
Concept - Topic Model Evaluation and Stability
Nov 08, 2026
Index: Concept Notes - Segmentation, Label Switching and Newer Topic Models
Nov 08, 2026
De Blasi 2015 - Consistency, Dependent Processes and Conclusions
Nov 08, 2026
De Blasi 2015 - EPPF and Predictive Structure
Nov 08, 2026
De Blasi 2015 - Gibbs-Type Priors - Overview
Nov 08, 2026
De Blasi 2015 - Gibbs-Type Priors Definition and Classification
Nov 08, 2026
De Blasi 2015 - Mixture Models and the Prior on the Number of Clusters
Nov 08, 2026
De Blasi 2015 - Species Sampling and Unseen Species Prediction
Nov 08, 2026
De Blasi 2015 - Subclasses Pitman-Yor NGG and Finite-Species Priors
Nov 08, 2026
Index: De Blasi 2015 - Gibbs-Type Priors
Nov 08, 2026
Griffiths 2011 - Applications of the IBP
Nov 08, 2026
Griffiths 2011 - Equivalence Classes and the Infinite Limit
Nov 08, 2026
Griffiths 2011 - Gibbs Sampling and the Linear-Gaussian Model
Nov 08, 2026
Griffiths 2011 - Infinite Mixtures and the CRP Review
Nov 08, 2026
Griffiths 2011 - Latent Feature Models and the Finite Beta-Bernoulli Prior
Nov 08, 2026
Griffiths 2011 - The Indian Buffet Process - Overview
Nov 08, 2026
Griffiths 2011 - The Indian Buffet Process and Its Properties
Nov 08, 2026
Griffiths 2011 - Two-Parameter IBP, Stick-Breaking and Beta Process
Nov 08, 2026
Index: Griffiths 2011 - The Indian Buffet Process
Nov 08, 2026
Teh 2010 - Blackwell-MacQueen Urn and Chinese Restaurant Process
Nov 08, 2026
Teh 2010 - DP Definition and Posterior Conjugacy
Nov 08, 2026
Teh 2010 - DP Mixture Models and Applications
Nov 08, 2026
Teh 2010 - Dirichlet Process - Overview
Nov 08, 2026
Teh 2010 - Extensions and Reference Map
Nov 08, 2026
Teh 2010 - Stick-Breaking Construction
Nov 08, 2026
Index: Teh 2010 - Dirichlet Process
Nov 08, 2026
Cinelli Hazlett 2020 - Bounding Confounder Strength with Observed Covariates
Nov 08, 2026
Cinelli Hazlett 2020 - Darfur Application and Reporting
Nov 08, 2026
Cinelli Hazlett 2020 - Informal Benchmarking Pitfalls and Related Methods
Nov 08, 2026
Cinelli Hazlett 2020 - Making Sense of Sensitivity - Overview
Nov 08, 2026
Cinelli Hazlett 2020 - Multiple and Nonlinear Confounders
Nov 08, 2026
Cinelli Hazlett 2020 - Partial R2 Parameterization and Bias Formulas
Nov 08, 2026
Cinelli Hazlett 2020 - Planted Confounders and Bayesian Sketch
Nov 08, 2026
Cinelli Hazlett 2020 - Robustness Value and Extreme Scenarios
Nov 08, 2026
Cinelli Hazlett 2020 - Traditional OVB and Sensitivity Contours
Nov 08, 2026
Franks 2019 - Calibrating Sensitivity Parameters
Nov 08, 2026
Franks 2019 - Discussion Limits and Bayesian Sketch
Nov 08, 2026
Franks 2019 - Flexible Sensitivity Analysis Without Observable Implications - Overview
Nov 08, 2026
Franks 2019 - Identified Sensitivity Parameters and Latent Confounder Pitfalls
Nov 08, 2026
Franks 2019 - Logistic Selection with Exponential Family Mixtures
Nov 08, 2026
Franks 2019 - NHANES and Job Training Applications
Nov 08, 2026
Franks 2019 - Tukey Factorization and Marginal Contrast Estimands
Nov 08, 2026
Imai 2010 - Bayesian Mediation Sketch for Halo Effects
Nov 08, 2026
Imai 2010 - Causal Mediation Analysis - Overview
Nov 08, 2026
Imai 2010 - Identification Compared with Pearl Robins and Principal Strata
Nov 08, 2026
Imai 2010 - LSEM Baron-Kenny Interaction and Estimation
Nov 08, 2026
Imai 2010 - Media Framing Application and Simulation
Nov 08, 2026
Imai 2010 - Potential Outcomes Mediation Effects and Sequential Ignorability
Nov 08, 2026
Imai 2010 - Sensitivity Analysis with Error Correlation Rho
Nov 08, 2026
Rosenman 2020 - Combining Observational and Experimental Datasets - Overview
Nov 08, 2026
Rosenman 2020 - Hierarchical Bias Model Sketch for Few Holdouts
Nov 08, 2026
Rosenman 2020 - Sensitivity Analysis and Implied Gamma
Nov 08, 2026
Rosenman 2020 - Setup Assumptions and Bias Structure
Nov 08, 2026
Rosenman 2020 - Simulation Results
Nov 08, 2026
Rosenman 2020 - Unbiased Risk Estimate and Common Shrinkage
Nov 08, 2026
Rosenman 2020 - Variance-Weighted Shrinkage and Practical Estimation
Nov 08, 2026
Unidentified Parameters - Keeping the Answer on the Question
Nov 08, 2026
Unidentified Parameters - Partial Identification and Bayesian Updating
Nov 08, 2026
Aleshin-Guendel 2020 - Identifiability of Latent Class MSE Models - Overview
Nov 08, 2026
Aleshin-Guendel 2020 - Latent Class Identifiability Theorem and LCMCR
Nov 08, 2026
Aleshin-Guendel 2020 - Mixed-Moments Theorem
Nov 08, 2026
Aleshin-Guendel 2020 - Mth Model and Identifiability Definition
Nov 08, 2026
Barker 2018 - N-Mixture Model and the Capture-Recapture View
Nov 08, 2026
Barker 2018 - Poisson Alternative and Model Discrimination
Nov 08, 2026
Barker 2018 - Relative Abundance, Alder Flycatcher Example and Discussion
Nov 08, 2026
Barker 2018 - Reliability of N-Mixture Models - Overview
Nov 08, 2026
Concept - Closed-Population Capture-Recapture and Otis Models
Nov 08, 2026
Concept - Latent Class and Dirichlet Process Models for Multiple Systems Estimation
Nov 08, 2026
Concept - Marginalising Latent Discrete Counts for HMC
Nov 08, 2026
Concept - Unseen Species and Latent Class Links
Nov 08, 2026
MacKenzie 2002 - Extensions, Design Guidance and PyMC Sketch
Nov 08, 2026
MacKenzie 2002 - Occupancy Model and Likelihood
Nov 08, 2026
MacKenzie 2002 - Simulation Study and Maryland Anuran Results
Nov 08, 2026
MacKenzie 2002 - Site Occupancy with Imperfect Detection - Overview
Nov 08, 2026
Silverman 2020 - Bayesian-Threshold Method
Nov 08, 2026
Silverman 2020 - Bayesian-Threshold Results and Conclusions
Nov 08, 2026
Silverman 2020 - Data Sets for Multiple Systems Estimation
Nov 08, 2026
Silverman 2020 - Existence, Identifiability and Empty Overlaps
Nov 08, 2026
Silverman 2020 - Frequentist Model Selection Results
Nov 08, 2026
Silverman 2020 - Madigan-York and Dirichlet Process Benchmarks
Nov 08, 2026
Silverman 2020 - Multiple Systems Analysis for Modern Slavery - Overview
Nov 08, 2026
Silverman 2020 - Poisson Log-Linear Model for Multiple Lists
Nov 08, 2026
Value of Information Concept Note - Adaptive Sampling and Acting on a Noisy Belief
Nov 08, 2026
Value of Information Concept Note - EVPI, EVPPI and EVSI
Nov 08, 2026
Value of Information Concept Note - Kriging with Known Measurement Error and EVSI for Monitor Siting
Nov 08, 2026
Value of Information Concept Note - Pricing a Model with EVPI
Nov 08, 2026
Value of Information Concept Note - Showing Uncertainty and Choosing a Display
Nov 08, 2026
Value of Information Concept Note - When an Exact Gaussian Process Earns Its Price
Nov 08, 2026
Heath 2015 - Discussion Limitations and Practical Guidance
Nov 08, 2026
Heath 2015 - Influenza Vaccine Case Study and Table 1
Nov 08, 2026
Heath 2015 - Nested Monte Carlo and Restrictive EVPPI Approximations
Nov 08, 2026
Heath 2015 - Net Benefit, EVPI and EVPPI Definitions
Nov 08, 2026
Heath 2015 - Regression-Based EVPPI (GAM and Gaussian Process)
Nov 08, 2026
Heath 2015 - Review of Expected Value of Information Methods - Overview
Nov 08, 2026
Heath 2015 - Single-Parameter EVPPI Strong-Oakley and Sadatsafavi
Nov 08, 2026
Hullman 2015 - Discussion and Limitations
Nov 08, 2026
Hullman 2015 - HOP Method and Related Work
Nov 08, 2026
Hullman 2015 - Hypothetical Outcome Plots - Overview
Nov 08, 2026
Hullman 2015 - Precision of Inference from HOPs
Nov 08, 2026
Hullman 2015 - Results and Mean Absolute Error
Nov 08, 2026
Hullman 2015 - Study Design and Distribution Tables
Nov 08, 2026
Kay 2016 - Discussion and Design Implications
Nov 08, 2026
Kay 2016 - Experiment Beta Regression and Results
Nov 08, 2026
Kay 2016 - Quantile Dotplot Construction
Nov 08, 2026
Kay 2016 - Transit User Survey and Design Requirements
Nov 08, 2026
Kay 2016 - When-ish is My Bus - Overview
Nov 08, 2026
Krause 2008 - Costs, Online Bounds and Exact Optimisation
Nov 08, 2026
Krause 2008 - Entropy versus Mutual Information Criteria
Nov 08, 2026
Krause 2008 - Experiments and Empirical Findings
Nov 08, 2026
Krause 2008 - GP Model and Posterior Variance
Nov 08, 2026
Krause 2008 - Greedy Algorithm and the 1-1 over e Guarantee
Nov 08, 2026
Krause 2008 - Lazy Evaluation and Local Kernels
Nov 08, 2026
Krause 2008 - Near-Optimal Sensor Placements in Gaussian Processes - Overview
Nov 08, 2026
Krause 2008 - Related Work and Why Other Criteria Lack Guarantees
Nov 08, 2026
Krause 2008 - Robust Placements
Nov 08, 2026
Vickers 2019 - Five-Step Interpretation and Prostate Biopsy Example
Nov 08, 2026
Vickers 2019 - Step-by-Step Guide to Decision Curve Analysis - Overview
Nov 08, 2026
Vickers 2019 - Ten Questions and Limits of Decision Curves
Nov 08, 2026
Concept Notes - Log-Pearson III Flood Frequency and Historic Floods
Nov 08, 2026
Concept Notes - Usual Intake from Repeated Short-Term Measurements
Nov 08, 2026
Greenacre 2023 - Aitchison CoDA 40 Years On - Overview
Nov 08, 2026
Greenacre 2023 - Compositional Outcome Models in PyMC
Nov 08, 2026
Greenacre 2023 - Logratios ALR CLR ILR and Log-Contrasts
Nov 08, 2026
Greenacre 2023 - Simplex, Closure and Core CoDA Properties
Nov 08, 2026
Greenacre 2023 - Tellus Application, Variable Selection and Correspondence Analysis
Nov 08, 2026
Greenacre 2023 - Wide Data, Zeros and Quasi-Coherence
Nov 08, 2026
Ji 2024 - Asymmetric Laplace Working Likelihood
Nov 08, 2026
Ji 2024 - Gibbs Posterior and Learning-Rate Calibration
Nov 08, 2026
Ji 2024 - Infinitesimal Jackknife Standard Errors
Nov 08, 2026
Ji 2024 - Simulation Results and Applications
Nov 08, 2026
Ji 2024 - Valid Standard Errors for Bayesian Quantile Regression - Overview
Nov 08, 2026
Ji 2024 - Yang-Wang-He Sandwich Adjustment
Nov 08, 2026
Pewsey 2021 - Circular Distributions and Constructions
Nov 08, 2026
Pewsey 2021 - Circular Models in PyMC
Nov 08, 2026
Pewsey 2021 - Circular Regression and Serial Dependence
Nov 08, 2026
Pewsey 2021 - Directional Statistics - Overview
Nov 08, 2026
Pewsey 2021 - Directional Statistics - Spherical, Toroidal and Other Topics
Nov 08, 2026
Pewsey 2021 - Inference, Uniformity Tests and Smeariness
Nov 08, 2026
Philip 2020 - Bayesian GEV Attribution in PyMC
Nov 08, 2026
Philip 2020 - Extreme Event Attribution Protocol - Overview
Nov 08, 2026
Philip 2020 - Extreme Value Distributions for Attribution
Nov 08, 2026
Philip 2020 - Model Evaluation, Multi-Model Attribution and Hazard Synthesis
Nov 08, 2026
Philip 2020 - Probability Framing, Event Definition and Return Periods
Nov 08, 2026
Philip 2020 - Trend Models and Natural Variability
Nov 08, 2026
Philip 2020 - Vulnerability, Exposure and Communication
Nov 08, 2026
Stasinopoulos 2007 - Bayesian P-Splines and Distributional Regression in PyMC
Nov 08, 2026
Stasinopoulos 2007 - Centile Curves, LMS and Residual Diagnostics
Nov 08, 2026
Stasinopoulos 2007 - Distributions and Additive Terms
Nov 08, 2026
Stasinopoulos 2007 - Fitting Algorithms and Model Selection
Nov 08, 2026
Stasinopoulos 2007 - GAMLSS Model Framework
Nov 08, 2026
Stasinopoulos 2007 - GAMLSS in R - Overview
Nov 08, 2026
Stasinopoulos 2007 - Lice, CD4 and Claims Examples
Nov 08, 2026
Tsagris 2018 - Dirichlet Regression with Zeros - Dirichlet Regression
Nov 08, 2026
Tsagris 2018 - Dirichlet Regression with Zeros - Discussion and Alternatives
Nov 08, 2026
Tsagris 2018 - Dirichlet Regression with Zeros - Examples and Simulation Results
Nov 08, 2026
Tsagris 2018 - Dirichlet Regression with Zeros - Overview
Nov 08, 2026
Tsagris 2018 - Dirichlet Regression with Zeros - Zero Adjusted Dirichlet Regression
Nov 08, 2026
Chen 2018 - Adjoint Sensitivity Method
Nov 08, 2026
Chen 2018 - Continuous Normalizing Flows
Nov 08, 2026
Chen 2018 - Latent ODE Time-Series Model
Nov 08, 2026
Chen 2018 - Neural ODEs - Overview
Nov 08, 2026
Chen 2018 - ODE-Nets for Supervised Learning
Nov 08, 2026
Chen 2018 - Scope, Limitations and Statistical Use
Nov 08, 2026
Chipman 2010 - BART - Overview
Nov 08, 2026
Chipman 2010 - Backfitting MCMC and Posterior Inference
Nov 08, 2026
Chipman 2010 - Drug Discovery Classification and Execution Time
Nov 08, 2026
Chipman 2010 - Extensions, Discussion and Practical Use
Nov 08, 2026
Chipman 2010 - Predictive Bake-Off and Friedman Simulations
Nov 08, 2026
Chipman 2010 - Probit BART for Classification
Nov 08, 2026
Chipman 2010 - Sum-of-Trees Model and Regularization Prior
Nov 08, 2026
Chaos, Rough Likelihoods and Data Assimilation (Concept Note)
Nov 08, 2026
Dose-Response with Weather Covariates in Long-Term Field Trials (Concept Note)
Nov 08, 2026
Jump-Diffusion and Stochastic Differential Equation Models (Concept Note)
Nov 08, 2026
Likelihoods Built on Differential Equations (Concept Note)
Nov 08, 2026
Luria-Delbruck and Telegraph Models as Compound Count Likelihoods (Concept Note)
Nov 08, 2026
Population PK Dosing Decisions from the Posterior (Concept Note)
Nov 08, 2026
Surplus-Production State-Space Models for Fisheries (Concept Note)
Nov 08, 2026
Universal Differential Equations and Scientific ML (Concept Note)
Nov 08, 2026
Izmailov 2021 - Bayesian Neural Network Posteriors - Overview
Nov 08, 2026
Izmailov 2021 - Cheaper Approximations versus HMC
Nov 08, 2026
Izmailov 2021 - Full-Batch HMC for Neural Networks
Nov 08, 2026
Izmailov 2021 - Mixing Diagnostics and Posterior Geometry
Nov 08, 2026
Izmailov 2021 - Predictive Performance and Domain Shift
Nov 08, 2026
Izmailov 2021 - Priors, Prior Predictive and Cold Posteriors
Nov 08, 2026
Margossian 2022 - Bayesian Pharmacometrics - Overview
Nov 08, 2026
Margossian 2022 - Compartment Models and Event Schedules
Nov 08, 2026
Margossian 2022 - Nonlinear PKPD and ODE Solvers
Nov 08, 2026
Margossian 2022 - Population Two-Compartment Model
Nov 08, 2026
Margossian 2022 - Posterior Predictive Checks and LOO
Nov 08, 2026
Margossian 2022 - Stan Workflow and MCMC Diagnostics
Nov 08, 2026
Banbura 2013 - Factor Models, Estimation and the Ragged Edge
Nov 08, 2026
Banbura 2013 - News Decomposition and Real-Time Vintages
Nov 08, 2026
Banbura 2013 - Partial Models and the Empirical GDP Application
Nov 08, 2026
Banbura 2013 - Sensor Fusion in State-Space Form (Concept Note)
Nov 08, 2026
Banbura et al 2013 - Now-casting and the Real-Time Data Flow - Overview
Nov 08, 2026
Gostic 2020 - Generation Interval and the Renewal Equation
Nov 08, 2026
Gostic 2020 - Observation Process, Smoothing Windows and Recommendations
Nov 08, 2026
Gostic 2020 - Practical Considerations for Measuring Rt - Overview
Nov 08, 2026
Gostic 2020 - Rt Estimation Pipeline and Right-Truncation Bias
Nov 08, 2026
Linzer 2013 - 2008 Application and Results
Nov 08, 2026
Linzer 2013 - Dynamic Bayesian Forecasting of Presidential Elections - Overview
Nov 08, 2026
Linzer 2013 - Extensions - House Effects, Correlated Errors and Election-Night Updating (Concept Note)
Nov 08, 2026
Linzer 2013 - Forecast Computation and Electoral College Simulation
Nov 08, 2026
Linzer 2013 - Reverse Random-Walk Model and Priors
Nov 08, 2026
McGough 2020 - Delay Dynamics, Moving Windows and Limitations
Nov 08, 2026
McGough 2020 - Evaluation Design and Results
Nov 08, 2026
McGough 2020 - NobBS Model and Priors
Nov 08, 2026
McGough 2020 - Nowcasting by Bayesian Smoothing - Overview
Nov 08, 2026
McGough 2020 - Reporting Triangle and Chain-Ladder Setup
Nov 08, 2026
Nowcasting Concepts - Delay Hazard Models and the Renewal Equation
Nov 08, 2026
Molina 2015 - Bayesian Fay-Herriot, Surprise Maps and Variance Functions (Concept Note)
Nov 08, 2026
Molina 2015 - Fay-Herriot Area-Level Model and EBLUP
Nov 08, 2026
Molina 2015 - Spatial and Spatio-Temporal Fay-Herriot Models
Nov 08, 2026
Molina 2015 - Unit-Level BHF Model and EB for Nonlinear Parameters
Nov 08, 2026
Molina Marhuenda 2015 - sae Small Area Estimation - Overview
Nov 08, 2026
Friedman 2000 - Bayesian Structure Learning Framework
Nov 08, 2026
Friedman 2000 - Being Bayesian about Network Structure - Overview
Nov 08, 2026
Friedman 2000 - Closed Form for a Known Ordering
Nov 08, 2026
Friedman 2000 - Experiments Mixing and Feature Recovery
Nov 08, 2026
Friedman 2000 - MCMC over Orderings and Computational Tricks
Nov 08, 2026
Hoff 2002 - Discussion Extensions and Stochastic Blockmodel Contrast
Nov 08, 2026
Hoff 2002 - Distance and Projection Models
Nov 08, 2026
Hoff 2002 - Latent Space Models Overview
Nov 08, 2026
Hoff 2002 - Likelihood Estimation and Procrustes MCMC
Nov 08, 2026
Hoff 2002 - Monks Florentine Families and Classroom Examples
Nov 08, 2026
Li 2019 - Graphical Horseshoe Estimator - Overview
Nov 08, 2026
Li 2019 - Graphical Horseshoe Model and Gibbs Sampler
Nov 08, 2026
Li 2019 - Kullback-Leibler Risk Bound and Bias Theory
Nov 08, 2026
Li 2019 - Simulation Study and Gene Expression Application
Nov 08, 2026
Network Outcome Models - Stochastic Block Models and Network Checks
Nov 08, 2026
Reinhart 2018 - Crime, Epidemic and Network Applications
Nov 08, 2026
Reinhart 2018 - ETAS Earthquake Models and Omori Law
Nov 08, 2026
Reinhart 2018 - Hawkes Processes and Conditional Intensity
Nov 08, 2026
Reinhart 2018 - Likelihood, EM and Bayesian Estimation
Nov 08, 2026
Reinhart 2018 - Self-Exciting Spatio-Temporal Point Processes - Overview
Nov 08, 2026
Reinhart 2018 - Simulation, Inference and Diagnostics
Nov 08, 2026
Reinhart 2018 - Stochastic Declustering
Nov 08, 2026
Riebler 2016 - BYM2 Intuitive Spatial Model - Overview
Nov 08, 2026
Riebler 2016 - Classical Disease Mapping Models Besag BYM Leroux Dean
Nov 08, 2026
Riebler 2016 - Penalised Complexity Priors for BYM2
Nov 08, 2026
Riebler 2016 - Scaling and the BYM2 Construction
Nov 08, 2026
Riebler 2016 - Simulation Study and Sardinia IDDM Application
Nov 08, 2026
Simpson 2016 - Convergence Theory
Nov 08, 2026
Simpson 2016 - Examples BCI Sampling Effort and Ocean
Nov 08, 2026
Simpson 2016 - Going Off Grid - Overview
Nov 08, 2026
Simpson 2016 - LGCP Likelihood and the Lattice Approximation
Nov 08, 2026
Simpson 2016 - Matern SPDE and GMRF Representation
Nov 08, 2026
Simpson 2016 - Off-Grid Poisson-Form Likelihood
Nov 08, 2026
Spatial Fields - Gaussian Random Fields, Kriging and Field-Level Inference
Nov 08, 2026
Bayesian Causal Discovery - BGe Score, Interventions and Sachs Data
Nov 08, 2026
Bayesian Gaussian Graphical Models - G-Wishart and Neighbourhood Selection
Nov 08, 2026
Phylogenetic Regression - Brownian Motion, OU and Pagel Lambda
Nov 08, 2026
Bürkner 2021 - Bayesian IRT with brms Overview
Nov 08, 2026
Bürkner 2021 - Covariates and Differential Item Functioning
Nov 08, 2026
Bürkner 2021 - IRT Model Classes 1PL 2PL and Extensions
Nov 08, 2026
Bürkner 2021 - Priors Hierarchical Pooling and Identification
Nov 08, 2026
Bürkner 2021 - Response Families and Distributional IRT
Nov 08, 2026
Bürkner 2021 - Worked Examples Binary Models VerbAgg
Nov 08, 2026
Bürkner 2021 - Worked Examples Ordinal and Response-Time Models
Nov 08, 2026
Bürkner 2021 - brms Specification Stan Code and Post-Processing
Nov 08, 2026
Caron 2012 - Bayesian Inference for Generalized Bradley-Terry Models - Overview
Nov 08, 2026
Caron 2012 - Bradley-Terry Likelihood and Latent Exponential Augmentation
Nov 08, 2026
Caron 2012 - Experiments NASCAR and Chess
Nov 08, 2026
Caron 2012 - Home Advantage, Ties and Team Comparisons
Nov 08, 2026
Caron 2012 - Identifiability, Hyperparameters and Further Extensions
Nov 08, 2026
Caron 2012 - Plackett-Luce Model for Full Rankings
Nov 08, 2026
Elo, Glicko and TrueSkill - Dynamic Skill Ratings (Concept Note)
Nov 08, 2026
Hui-Walter and Latent Class Models for Diagnostic Tests (Concept Note)
Nov 08, 2026
Item Response Theory Norms and You Are Here Placement (Concept Note)
Nov 08, 2026
Market Structure Analysis - IIA, Nested Logit and Choice Maps (Concept Note)
Nov 08, 2026
Elrod and Keane 1995 - Empirical Assessment on Detergent Panel Data
Nov 08, 2026
Elrod and Keane 1995 - Factor-Analytic Probit Market Structure - Overview
Nov 08, 2026
Elrod and Keane 1995 - Factor-Analytic Probit Model Specification
Nov 08, 2026
Elrod and Keane 1995 - Models for Internal Market Structure Analysis
Nov 08, 2026
Elrod and Keane 1995 - Simulated Moments and Panel Estimation
Nov 08, 2026
Paun 2018 - Comparing Bayesian Models of Annotation - Overview
Nov 08, 2026
Paun 2018 - Evaluation Protocol and Results Tables
Nov 08, 2026
Paun 2018 - Implementation Priors Label Switching and Guidelines
Nov 08, 2026
Paun 2018 - Partially Pooled Models Hierarchical D&S ItemDiff LogRndEff
Nov 08, 2026
Paun 2018 - Pooled and Unpooled Models Multinomial Dawid-Skene MACE
Nov 08, 2026
Weng and Lin 2011 - Bayesian Online Ranking Overview
Nov 08, 2026
Weng and Lin 2011 - Bradley-Terry Update Algorithms
Nov 08, 2026
Weng and Lin 2011 - Glicko Derivation and Comparison with Elo
Nov 08, 2026
Weng and Lin 2011 - Halo 2 Experiments and Results
Nov 08, 2026
Weng and Lin 2011 - Ranking Models and the W-Stein Approximation
Nov 08, 2026
Weng and Lin 2011 - Thurstone-Mosteller and Plackett-Luce Update Rules
Nov 08, 2026
Weng and Lin 2011 - Update Rules Derivation and Error Correction
Nov 08, 2026
Expected Information Gain
Nov 08, 2026
Confirmatory Factor Analysis and Structural Equation Models
Nov 08, 2026
Factor Analysis and Probabilistic PCA
Nov 08, 2026
Hilbert Space Gaussian Processes (HSGPs)
Nov 08, 2026
Monsters and Mixtures
Nov 08, 2026
Bayesian Non-parametric Causal Inference
Nov 08, 2026
Nonparametric Models Overview
Nov 08, 2026
Social Network Models
Nov 08, 2026
Spatial Models — Besag-York-Mollie (BYM)
Nov 08, 2026
Propensity Score in Bayesian Causal Inference
Nov 08, 2026
Potential Outcomes Framework
Nov 08, 2026
Sensitivity Analysis in Observational Studies
Nov 08, 2026
Bayesian Structural Time-Series Model
Nov 08, 2026
Linear-Gaussian State-Space Models
Nov 08, 2026
Marginal Likelihood via the Kalman Filter
Nov 08, 2026
The Kalman Filter
Nov 08, 2026
Efficient MCMC
Nov 08, 2026
HMC and Stan in Practice
Nov 08, 2026
Hierarchical Models
Nov 08, 2026
Partial Pooling as Multiple Comparisons Correction
Nov 08, 2026
Decision Analysis
Nov 08, 2026
Generalized Linear Models
Nov 08, 2026
Hierarchical Linear Models
Nov 08, 2026
Horseshoe and Regularized Horseshoe Priors
Nov 08, 2026
Chaos and Phase-Insensitive Statistics
Nov 08, 2026
Synthetic Likelihood - Overview
Nov 08, 2026
From Inference to Decision
Nov 08, 2026
Joint Priors and Covariance Matrices
Nov 08, 2026
Poststratification
Nov 08, 2026
Prior Predictive Checking
Nov 08, 2026
Challenge of Multimodality - Differential Equation for Planetary Motion
Nov 08, 2026
Model Building with Latent Variables - Animal Movement
Nov 08, 2026
Sampling Problems with Latent Variables - No Vehicles in the Park
Nov 08, 2026
The Typical Set and the Log Posterior Density
Nov 08, 2026
Posterior Predictive Checking
Nov 08, 2026
Simulation-Based Calibration - Overview
Nov 08, 2026
Causal Discovery - Overview
Nov 08, 2026
GES and Score-Based Discovery
Nov 08, 2026
Factor Copulas - Overview
Nov 08, 2026
Discrete Choice Models
Nov 08, 2026
Quantile Regression
Nov 08, 2026
Omitted Variables Bias
Nov 08, 2026
Andrews Kasy 2019 - Identification of and Correction for Publication Bias - Overview
Nov 08, 2026
Bayesian Estimation and Priors for MMM
Nov 08, 2026
Gordon et al 2019 - Sensitivity to Unobservables and Bias-Reduction Ratio
Nov 08, 2026
Gaussian Process Regression
Nov 08, 2026
Acquisition Functions
Nov 08, 2026
Bayesian Optimisation
Nov 08, 2026
Survival Analysis
Nov 08, 2026
Type S and Type M Errors
Nov 08, 2026
Bayesian Inverse Probability Weighting
Nov 08, 2026
Causal Estimands
Nov 08, 2026
Do-Calculus in Summary Causal DAGs: Soundness, Completeness, and ATE Computation
Nov 08, 2026
Overfitting and Information Criteria
Nov 08, 2026
Constructing Priors for Effect Sizes
Nov 08, 2026
Doubly-Robust Estimands for ATT(g,t)
Nov 08, 2026
Directed Acyclic Graphs
Nov 08, 2026
The Experimental Ideal
Nov 08, 2026
DAGs and Causal Identification
Nov 08, 2026
Dehghani 2021 - Community Bias and the GLUE Example
Nov 08, 2026
Dehghani 2021 - Guidelines, Statistical Testing and Living Benchmarks
Nov 08, 2026
Dehghani 2021 - Life of a Benchmark
Nov 08, 2026
Dehghani 2021 - Reviewer Checklist and Marketing Measurement Applications
Nov 08, 2026
Dehghani 2021 - Rigging the Lottery - RecSys and ALE
Nov 08, 2026
Dehghani 2021 - Score and Rank Aggregation and Human Evaluation Bias
Nov 08, 2026
Dehghani 2021 - Stateful Benchmarks and Adaptive Overfitting
Nov 08, 2026
Dehghani 2021 - Task Selection Bias Case Studies
Nov 08, 2026
Dehghani 2021 - The Benchmark Lottery - Overview
Nov 08, 2026
Deng 2009 - Applications and Future Work
Nov 08, 2026
Deng 2009 - Construction Pipeline and AMT Quality Control
Nov 08, 2026
Deng 2009 - Dataset Properties and Comparison with Other Datasets
Nov 08, 2026
Deng 2009 - ImageNet - Overview
Nov 08, 2026
Index: Deng 2009 - ImageNet
Nov 08, 2026
Dorie 2019 - ACIC 2016 Causal Inference Competition - Overview
Nov 08, 2026
Dorie 2019 - Discussion, Lessons and Marketing Measurement Relevance
Nov 08, 2026
Dorie 2019 - Estimands, Ignorability and Design Choices
Nov 08, 2026
Dorie 2019 - Evaluation Metrics and Results by Method
Nov 08, 2026
Dorie 2019 - Motivation and Shortcomings of Method Comparisons
Nov 08, 2026
Dorie 2019 - Predicting Performance and Variance Decomposition
Nov 08, 2026
Dorie 2019 - Simulation Design and Knobs
Nov 08, 2026
Dorie 2019 - Submitted Methods and Taxonomy
Nov 08, 2026
Dorie 2019 - Toy ACIC-Style Simulation in Python
Nov 08, 2026
Gebru 2021 - Datasheets for Datasets - Overview
Nov 08, 2026
Gebru 2021 - Example Datasheet for the Movie Review Polarity Dataset
Nov 08, 2026
Gebru 2021 - Impact, Challenges and a Marketing Measurement Datasheet
Nov 08, 2026
Gebru 2021 - Motivation, Objectives and Development Process
Nov 08, 2026
Gebru 2021 - Questions I - Motivation, Composition and Collection Process
Nov 08, 2026
Gebru 2021 - Questions II - Preprocessing, Uses, Distribution and Maintenance
Nov 08, 2026
Liang 2022 - Appendix Guide
Nov 08, 2026
Liang 2022 - Core Scenarios and Dataset Selection
Nov 08, 2026
Liang 2022 - Experiments Meta-Analysis and Prompting Ablations
Nov 08, 2026
Liang 2022 - Findings 1 to 13 Core Scenarios
Nov 08, 2026
Liang 2022 - Findings 14 to 25 Targeted Evaluations and Prompting
Nov 08, 2026
Liang 2022 - HELM - Overview
Nov 08, 2026
Liang 2022 - Holistic Evaluation Framework and Taxonomy
Nov 08, 2026
Liang 2022 - Models and Adaptation via Prompting
Nov 08, 2026
Liang 2022 - Related Work, Missing Pieces and Limitations
Nov 08, 2026
Liang 2022 - Targeted Evaluations
Nov 08, 2026
Liang 2022 - The Seven Metrics
Nov 08, 2026
Index: Liang 2022 - Holistic Evaluation of Language Models
Nov 08, 2026
Wang 2018 - Appendices and Implementation Details
Nov 08, 2026
Wang 2018 - Baselines and Benchmark Results
Nov 08, 2026
Wang 2018 - Diagnostic Dataset
Nov 08, 2026
Wang 2018 - Diagnostic Results and Analysis
Nov 08, 2026
Wang 2018 - Evaluation Protocol and Scoring
Nov 08, 2026
Wang 2018 - GLUE - Overview
Nov 08, 2026
Wang 2018 - Task Suite and Selection Criteria
Nov 08, 2026
Index: Wang 2018 - GLUE
Nov 08, 2026
Allcott 2015 - Conclusions, Remedies and Marketing Implications
Nov 08, 2026
Allcott 2015 - Motivating Evidence from Microfinance and Clinical Trials
Nov 08, 2026
Bareinboim 2016 - SCM Principles and Confounding Bias
Nov 08, 2026
Bareinboim 2016 - Sample Selection Bias and Recoverability
Nov 08, 2026
Bareinboim 2016 - Transportability and Data Fusion
Nov 08, 2026
Bareinboim Pearl 2016 - Causal Inference and Data Fusion - Overview
Nov 08, 2026
Dahabreh 2020 - CASS Coronary Surgery Application
Nov 08, 2026
Dahabreh 2020 - Extending Inferences from a Randomized Trial - Overview
Nov 08, 2026
Dahabreh 2020 - Simulation Study of Estimators
Nov 08, 2026
Degtiar 2023 - Assessing Dissimilarity Between Study and Target
Nov 08, 2026
Degtiar 2023 - Doubly Robust and Combined Methods
Nov 08, 2026
Degtiar 2023 - Identifying Assumptions
Nov 08, 2026
Degtiar 2023 - Outcome Regression Methods
Nov 08, 2026
Degtiar 2023 - Populations, Estimands and External Validity Bias
Nov 08, 2026
Degtiar 2023 - Recommendations, Summary-Level Methods and Comparison Tables
Nov 08, 2026
Degtiar 2023 - Testing Treatment Effect Heterogeneity
Nov 08, 2026
Degtiar 2023 - Weighting and Matching Methods
Nov 08, 2026
Degtiar Rose 2023 - Generalizability and Transportability Review - Overview
Nov 08, 2026
Pearl 2014 - Appendix Derivations of Transport Formulas
Nov 08, 2026
Pearl 2014 - Conclusions, Extensions and Limits
Nov 08, 2026
Pearl 2014 - Marketing Application Transporting Geo and Lift Results
Nov 08, 2026
Pearl 2014 - Motivating Examples and Transport Formulas
Nov 08, 2026
Pearl 2014 - S-Admissibility and the Graphical Criterion
Nov 08, 2026
Pearl 2014 - Selection Diagrams and Definition of Transportability
Nov 08, 2026
Pearl 2014 - Threats vs Licenses and SCM Preliminaries
Nov 08, 2026
Pearl Bareinboim 2014 - External Validity and Transportability - Overview
Nov 08, 2026
Abadi 2016 - DP Background and Gaussian Mechanism
Nov 08, 2026
Abadi 2016 - DP-SGD Algorithm and Implementation
Nov 08, 2026
Abadi 2016 - Deep Learning with Differential Privacy - Overview
Nov 08, 2026
Abadi 2016 - Hyperparameter Tuning and Privacy Cost of Search
Nov 08, 2026
Abadi 2016 - MNIST and CIFAR-10 Experiments
Nov 08, 2026
Abadi 2016 - Moments Accountant Theorems and Proofs
Nov 08, 2026
Abadi 2016 - Related Work, Limitations and Marketing Relevance
Nov 08, 2026
Abowd 2022 - 2020 Census TopDown Algorithm - Overview
Nov 08, 2026
Abowd 2022 - Conclusion, Limitations and Marketing Relevance
Nov 08, 2026
Abowd 2022 - Discrete Gaussian, Queries and the Optimization Problem
Nov 08, 2026
Abowd 2022 - Geography Spines, Computing Environment and Block-by-Block Baseline
Nov 08, 2026
Abowd 2022 - Least Squares and Rounder Estimators
Nov 08, 2026
Abowd 2022 - Policy Constraints, Invariants and the Redistricting Schema
Nov 08, 2026
Abowd 2022 - Privacy-Loss Budget Allocation and Tuning
Nov 08, 2026
Abowd 2022 - The TopDown Algorithm and Privacy Accounting Proof
Nov 08, 2026
Abowd 2022 - Utility Experiments
Nov 08, 2026
Abowd 2022 - Why Differential Privacy, zCDP and Curator Models
Nov 08, 2026
Dong Roth Su 2022 - Composition Theorem and Central Limit Theorems
Nov 08, 2026
Dong Roth Su 2022 - Composition of Approximate DP and Berry-Esseen
Nov 08, 2026
Dong Roth Su 2022 - Divergence Conversion, Discussion and Limitations
Nov 08, 2026
Dong Roth Su 2022 - Gaussian Differential Privacy - Overview
Nov 08, 2026
Dong Roth Su 2022 - Gaussian Differential Privacy Definition and Gaussian Mechanism
Nov 08, 2026
Dong Roth Su 2022 - Noisy SGD Privacy Analysis
Nov 08, 2026
Dong Roth Su 2022 - Post-Processing, Blackwell, Primal-Dual and Group Privacy
Nov 08, 2026
Dong Roth Su 2022 - Privacy Amplification by Subsampling
Nov 08, 2026
Dong Roth Su 2022 - Trade-off Functions and f-DP
Nov 08, 2026
Dwork 2006 - Calibrating Noise to Sensitivity - Overview
Nov 08, 2026
Dwork 2006 - Differential Privacy Definition and Basic Examples
Nov 08, 2026
Dwork 2006 - Interactive versus Non-Interactive Separation Results
Nov 08, 2026
Dwork 2006 - Low-Sensitivity Functions and Applications
Nov 08, 2026
Dwork 2006 - Motivation, Contributions and Related Work
Nov 08, 2026
Dwork 2006 - Postprocessing, Composition and Group Privacy
Nov 08, 2026
Dwork 2006 - Proof of the Separation Lemma
Nov 08, 2026
Dwork 2006 - Sensitivity and the Laplace Mechanism
Nov 08, 2026
Dwork 2006 - Sensitivity in General Metric Spaces
Nov 08, 2026
Dwork Roth 2014 - Advanced Composition and the Gaussian Mechanism
Nov 08, 2026
Dwork Roth 2014 - Algorithmic Foundations of Differential Privacy Ch 1-3 - Overview
Nov 08, 2026
Dwork Roth 2014 - Basic Composition and Divergence Tools
Nov 08, 2026
Dwork Roth 2014 - Differential Privacy Definition and Model of Computation
Nov 08, 2026
Dwork Roth 2014 - Laplace Mechanism
Nov 08, 2026
Dwork Roth 2014 - Motivation, Attacks and Why Anonymization Fails
Nov 08, 2026
Dwork Roth 2014 - Post-Processing, Group Privacy and What DP Promises
Nov 08, 2026
Dwork Roth 2014 - Randomized Response
Nov 08, 2026
Dwork Roth 2014 - Sparse Vector Technique
Nov 08, 2026
Mironov 2017 - Appendix Renyi Divergence Properties
Nov 08, 2026
Mironov 2017 - Discussion, Budget Curves and Open Questions
Nov 08, 2026
Mironov 2017 - From RDP to Approximate DP and Advanced Composition
Nov 08, 2026
Mironov 2017 - RDP Properties and Composition
Nov 08, 2026
Mironov 2017 - RDP of Randomized Response, Laplace and Gaussian
Nov 08, 2026
Mironov 2017 - Renyi Differential Privacy - Overview
Nov 08, 2026
Mironov 2017 - Renyi Divergence and the RDP Definition
Nov 08, 2026
Wasserman Zhou 2010 - A Statistical Framework for Differential Privacy - Overview
Nov 08, 2026
Wasserman Zhou 2010 - Differential Privacy as a Hypothesis-Testing Constraint
Nov 08, 2026
Wasserman Zhou 2010 - Exponential Mechanism and Small Ball Probabilities
Nov 08, 2026
Wasserman Zhou 2010 - Histogram Release Mechanisms
Nov 08, 2026
Wasserman Zhou 2010 - Informative Mechanisms and the Impossibility Theorem
Nov 08, 2026
Wasserman Zhou 2010 - Orthogonal Series Density Estimation
Nov 08, 2026
Wasserman Zhou 2010 - Rate Tables, Simulation and Open Questions
Nov 08, 2026
Wasserman Zhou 2010 - Setup, Data Release Mechanisms and Differential Privacy
Nov 08, 2026
Andrews Kasy 2019 - Conclusion, Optimal Publication Rules and Limitations
Nov 08, 2026
Andrews Kasy 2019 - Estimation and Replication Applications in Economics and Psychology
Nov 08, 2026
Andrews Kasy 2019 - Identification Using Meta-Studies
Nov 08, 2026
Andrews Kasy 2019 - Identification Using Replication Studies
Nov 08, 2026
Andrews Kasy 2019 - Median-Unbiased Estimation and Corrected Confidence Sets
Nov 08, 2026
Andrews Kasy 2019 - Meta-Study Applications - Minimum Wage and Deworming
Nov 08, 2026
Andrews Kasy 2019 - Relation to Existing Approaches and Meta-Regressions
Nov 08, 2026
Andrews Kasy 2019 - Setting and the Truncated Sampling Model
Nov 08, 2026
Index: Andrews Kasy 2019 - Identification of and Correction for Publication Bias
Nov 08, 2026
IntHout 2014 - Cochrane Empirical Comparison
Nov 08, 2026
IntHout 2014 - Converting DL Results to HKSJ Worked Examples
Nov 08, 2026
IntHout 2014 - Discussion, Limitations and Recommendations
Nov 08, 2026
IntHout 2014 - HKSJ and DerSimonian-Laird Formal Specification
Nov 08, 2026
IntHout 2014 - HKSJ vs DerSimonian-Laird Random-Effects Meta-Analysis - Overview
Nov 08, 2026
IntHout 2014 - Simulation Design
Nov 08, 2026
IntHout 2014 - Simulation Results and Error Rates
Nov 08, 2026
Meager 2019 - Average Treatment Effects - Results
Nov 08, 2026
Meager 2019 - Bayesian Estimation, Priors and Computation
Nov 08, 2026
Meager 2019 - Bayesian Hierarchical Analysis of Microcredit - Overview
Nov 08, 2026
Meager 2019 - Conclusion, Limitations and Marketing Analogues
Nov 08, 2026
Meager 2019 - Covariates - Prior Business Experience and Site-Level Ridge
Nov 08, 2026
Meager 2019 - Heterogeneity, Pooling Results and Predictive Distributions
Nov 08, 2026
Meager 2019 - Hierarchical Model Specifications
Nov 08, 2026
Meager 2019 - Pooling Factors and Measuring External Validity
Nov 08, 2026
Meager 2019 - Studies, Data and Outcomes
Nov 08, 2026
Index: Meager 2019 - Bayesian Hierarchical Analysis of Microcredit
Nov 08, 2026
Roever 2020 - Bayesian Random-Effects Meta-Analysis with bayesmeta - Overview
Nov 08, 2026
Roever 2020 - DIRECT Algorithm Implementation and Calibration
Nov 08, 2026
Roever 2020 - Marginal Likelihood and Conditional Posteriors
Nov 08, 2026
Roever 2020 - Normal-Normal Hierarchical Model and Likelihood
Nov 08, 2026
Roever 2020 - Overview and Motivation of Bayesian Meta-Analysis
Nov 08, 2026
Roever 2020 - Pediatric Liver Transplant Worked Example
Nov 08, 2026
Roever 2020 - Posterior Predictive Checks and Frequentist Connection
Nov 08, 2026
Roever 2020 - Prediction, Shrinkage and Credible Intervals
Nov 08, 2026
Roever 2020 - Priors for the Effect and Heterogeneity
Nov 08, 2026
Sterne 2011 - Funnel Plot Asymmetry - Overview
Nov 08, 2026
Sterne 2011 - Funnel Plots and the Causes of Asymmetry
Nov 08, 2026
Sterne 2011 - Magnesium Example, Model Choice and Adjustment
Nov 08, 2026
Sterne 2011 - Tests for Asymmetry and the Egger Regression
Nov 08, 2026
Index: Sterne 2011 - Funnel Plot Asymmetry
Nov 08, 2026
Viechtbauer 2010 - BCG Vaccine Worked Example
Nov 08, 2026
Viechtbauer 2010 - Conducting Meta-Analyses in R with the metafor Package - Overview
Nov 08, 2026
Viechtbauer 2010 - Effect Size Measures and escalc
Nov 08, 2026
Viechtbauer 2010 - Fitting Models with rma, Moderators and Knapp-Hartung
Nov 08, 2026
Viechtbauer 2010 - Fixed, Random and Mixed-Effects Models
Nov 08, 2026
Viechtbauer 2010 - LRT, Permutation Tests, BLUPs and Model Fit
Nov 08, 2026
Viechtbauer 2010 - Publication Bias, Trim-and-Fill and Cumulative Meta-Analysis
Nov 08, 2026
Viechtbauer 2010 - Residuals, Influence Diagnostics and Plots
Nov 08, 2026
Viechtbauer 2010 - Technical Details, Validation and Package Comparison
Nov 08, 2026
Viechtbauer 2010 - tau2 Estimators, Q-Test and Heterogeneity Statistics
Nov 08, 2026
Geo-Experiment Design and Power Analysis
Nov 08, 2026
Geo-Experiment Methodology - Overview
Nov 08, 2026
Gordon et al 2019 - Facebook RCTs vs Observational Ad Measurement - Overview
Nov 08, 2026
Manheim Garrabrant 2018 - Variants of Goodhart's Law - Overview
Nov 08, 2026
Ioannidis 2005 - Why Most Published Findings Are False - Overview
Nov 08, 2026
Morris White Crowther 2019 - Simulation Studies (ADEMP) - Overview
Nov 08, 2026
Multiple Testing Corrections
Nov 08, 2026
Power Analysis and Sample Size
Nov 08, 2026
Garden of Forking Paths
Nov 08, 2026
Researcher Degrees of Freedom
Oct 13, 2026
ABM in Marketing Strategy
Oct 13, 2026
Spike-and-Slab Prior for Covariate Selection
Oct 13, 2026
Metalearners for CATE
Oct 13, 2026
Garden of Forking Data
Oct 13, 2026
Global-Local Shrinkage Priors
Oct 13, 2026
Spurious Association and Confounds
Oct 13, 2026
Designing Simulated-Data Experiments
Oct 13, 2026
Effective Sample Size and Monte Carlo Standard Error
Oct 13, 2026
How Many Digits to Report
Oct 13, 2026
What to Do About Convergence Problems
Oct 13, 2026
Cross Validation Checking
Oct 13, 2026
Influence of Likelihood and Prior
Oct 13, 2026
Model Selection Using Predictive Performance
Oct 13, 2026
Model Selection and Overfitting
Oct 13, 2026
The Replication Crisis and Multiple Levels of Variation
Oct 13, 2026
There Is No Safe Haven
Oct 13, 2026
Modeling as Software Development
Oct 13, 2026
The SBC Algorithm
Oct 13, 2026
Propensity Score Matching - Balancing Theorem and Failure Modes
Oct 13, 2026
Regression Discontinuity Designs
Oct 13, 2026
Conditional Independence Assumption
Oct 13, 2026
Carryover (Adstock) Functional Forms
Oct 13, 2026
Jin 2017 MMM - Conclusions, Limitations and Extensions
Oct 13, 2026
Jin 2017 MMM - Prior Sensitivity Experiments on Beta and Half-Saturation
Oct 13, 2026
Jin 2017 MMM - STAN Sampler Code and MCMC Implementation
Oct 13, 2026
Jin 2017 MMM - Sample Size vs Bias Study (Two vs Sixty Years)
Oct 13, 2026
Jin 2017 MMM - Shampoo Case Study Data, Diagnostics and Posteriors
Oct 13, 2026
Jin 2017 MMM - Simulated ROAS, mROAS and Optimization Results
Oct 13, 2026
Jin 2017 MMM - Simulation Design, Signal Strength and Priors
Oct 13, 2026
MMM Model Selection and Application
Oct 13, 2026
ROAS, mROAS, and Optimal Media Mix
Oct 13, 2026
Shape (Saturation) Effects
Oct 13, 2026
Gordon et al 2019 - Conclusions, Caveats and Related Literature
Oct 13, 2026
Gordon et al 2019 - Facebook Experiment Design and Exposure Endogeneity
Oct 13, 2026
Gordon et al 2019 - Observational Lift Estimates by Study and Method
Oct 13, 2026
Gordon et al 2019 - Propensity Estimation, Bootstrap Inference and Balance Diagnostics
Oct 13, 2026
Gordon et al 2019 - RCT Estimands, Unconfoundedness and Six Observational Estimators
Oct 13, 2026
Gordon et al 2019 - RCT ITT and ATT Lifts for 15 Studies
Oct 13, 2026
Gordon et al 2019 - Reference List and Cited Literature
Oct 13, 2026
Gordon et al 2019 - Study Sample, Covariate Sets and Randomization Check
Oct 13, 2026
Lewis Rao 2015 - Market Implications, Persuasion Games and Conclusion
Oct 13, 2026
Lewis Rao 2015 - Reference List and Cited Literature
Oct 13, 2026
Lewis Rao 2015 - Robustness, Scale, Targeting and Measurement Technology
Oct 13, 2026
Lewis Rao 2015 - Table I Summary of the 25 Yahoo Display Experiments
Oct 13, 2026
Lewis Rao 2015 - Table II Estimation Protocol, Windows and ROI Standard Errors
Oct 13, 2026
Lewis Rao 2015 - Table III Power and Experiment Size Multipliers
Oct 13, 2026
Lewis Rao 2015 - The Advertiser's Problem and Marginal ROI
Oct 13, 2026
Lewis Rao 2015 - Unfavorable Economics of Ad Measurement - Overview
Oct 13, 2026
Blake 2015 - Brand Keyword Experiments and Paid-Organic Substitution
Oct 13, 2026
Blake 2015 - Discussion, Limitations and Lessons for Marketing Measurement
Oct 13, 2026
Blake 2015 - Non-Brand DMA Geo Experiment and DiD Estimates
Oct 13, 2026
Blake 2015 - Non-Brand Traffic Substitution (Clicks vs Visits)
Oct 13, 2026
Blake 2015 - OLS vs IV Spend Elasticities and the Endogeneity Gap
Oct 13, 2026
Blake 2015 - ROI Derivation from Experimental Log-Sales Effects
Oct 13, 2026
Blake 2015 - SEM Mechanics and the Endogeneity of Click-Priced Spend
Oct 13, 2026
Blake 2015 - Working Paper vs Econometrica Version Differences
Oct 13, 2026
Blake Nosko Tadelis 2015 - Paid Search Effectiveness - Overview
Oct 13, 2026
Dillon et al - Non-Confounding Redundancy and the Control-or-Drop Rule
Oct 13, 2026
Dillon et al - Observed Confounding and Double ML for Ad Spend
Oct 13, 2026
Dillon et al - Prediction Tasks vs Causal Tasks
Oct 13, 2026
Dillon et al - SHAP Plots vs True Causal Effects
Oct 13, 2026
Dillon et al - SHAP and Causal Insights - Overview
Oct 13, 2026
Dillon et al - Subscriber Retention Data-Generating Process
Oct 13, 2026
Dillon et al - Summary, Limitations and Marketing Implications
Oct 13, 2026
Dillon et al - Unobserved Confounding and Quasi-Experimental Remedies
Oct 13, 2026
Dillon et al - When SHAP Recovers Causal Effects
Oct 13, 2026
Janzing 2020 - Baseline Attribution Axioms and Integrated Gradients
Oct 13, 2026
Janzing 2020 - Conclusions, Limitations and Model-vs-World Attribution
Oct 13, 2026
Janzing 2020 - Conditional vs Marginal Dropping of Features
Oct 13, 2026
Janzing 2020 - Inputs as Causes - Interventional Equals Marginal Expectation
Oct 13, 2026
Janzing 2020 - Irrelevant-Feature Example and Failure of Sensitivity
Oct 13, 2026
Janzing 2020 - KernelSHAP vs SHAPR Experiments
Oct 13, 2026
Janzing 2020 - Symmetry Axiom Objection and Reply
Oct 13, 2026
Janzing et al 2020 - Feature Relevance as a Causal Problem - Overview
Oct 13, 2026
Kumar et al - Additivity Axiom Limits for Non-Additive Models
Oct 13, 2026
Kumar et al - Conditional vs Interventional Shapley Catch-22
Oct 13, 2026
Kumar et al - Shapley Values as Contrastive and Actionable Explanations
Oct 13, 2026
Kumar et al - Shapley Values in Normative Model Evaluation
Oct 13, 2026
Kumar et al 2020 - Problems with Shapley-Value Explanations - Overview
Oct 13, 2026
Shapley Values and SHAP Value Functions
Oct 13, 2026
Manheim Garrabrant - Adversarial Goodhart, Campbell's Law and Cobra Effects
Oct 13, 2026
Manheim Garrabrant - Causal Goodhart via Shared Cause, Intermediary and Metric Manipulation
Oct 13, 2026
Manheim Garrabrant - Extremal Goodhart, Model Insufficiency and Regime Change
Oct 13, 2026
Manheim Garrabrant - Goodhart Setup with Regulator, Goal and Proxy
Oct 13, 2026
Manheim Garrabrant - Goodhart Variants in Marketing Measurement and Model Validation
Oct 13, 2026
Manheim Garrabrant - Non-Causal Goodhart Effects in Causal Systems
Oct 13, 2026
Manheim Garrabrant - Regressional Goodhart and Tails Coming Apart
Oct 13, 2026
Fisher Rudin Dominici 2019 - Model Class Reliance - Overview
Oct 13, 2026
Model Class Reliance - Appendix B Proofs of the Finite-Sample Bounds
Oct 13, 2026
Model Class Reliance - Appendix C Proofs of the Binary Search Lemmas
Oct 13, 2026
Model Class Reliance - Bibliographic Details and Reference List
Oct 13, 2026
Model Class Reliance - Binary Search Computation
Oct 13, 2026
Model Class Reliance - Discussion, Limitations and Extensions
Oct 13, 2026
Model Class Reliance - Outer and Inner Finite-Sample Bounds
Oct 13, 2026
Model Class Reliance - Recidivism Case Study
Oct 13, 2026
Model Class Reliance - Toy Example and Bootstrap Coverage Simulations
Oct 13, 2026
Model Class Reliance for Linear and RKHS Model Classes
Oct 13, 2026
Model Reliance - Permutation Importance as a U-Statistic
Oct 13, 2026
Model Reliance, Causal Effects and Conditional Model Reliance
Oct 13, 2026
Rashomon Sets and Model Class Reliance - Definitions
Oct 13, 2026
Rashomon-Set Confidence Intervals and Profile Likelihood
Oct 13, 2026
Hooker Mentch Zhou 2021 - Conditional and Relearning Importance Measures
Oct 13, 2026
Hooker Mentch Zhou 2021 - Extrapolation Mechanism in Forests and Neural Nets
Oct 13, 2026
Hooker Mentch Zhou 2021 - Linear Simulation Design and Theorem 1
Oct 13, 2026
Hooker Mentch Zhou 2021 - Partial Dependence Alternatives and Conclusions
Oct 13, 2026
Hooker Mentch Zhou 2021 - Permute-and-Predict Extrapolation - Overview
Oct 13, 2026
Hooker Mentch Zhou 2021 - References and Acknowledgments
Oct 13, 2026
Hooker Mentch Zhou 2021 - Relearning Hypothesis Tests and Bike-Share Application
Oct 13, 2026
Permute-and-Predict Extrapolation in Variable Importance
Oct 13, 2026
Meinshausen Buhlmann 2010 - Stability Selection - Overview
Oct 13, 2026
Randomised Lasso Consistency under Stability Selection (Theorem 2)
Oct 13, 2026
Randomised Lasso and the Irrepresentable Condition
Oct 13, 2026
Randomised Orthogonal Matching Pursuit (ROMP) for Stability Selection
Oct 13, 2026
Stability Paths and the Stable-Variable Set
Oct 13, 2026
Stability Selection (Meinshausen Buhlmann 2010) - Bibliography and Cited Works
Oct 13, 2026
Stability Selection - Discussion, Limitations and Practice
Oct 13, 2026
Stability Selection Figs 5-6 - Per-Cell Power Readings
Oct 13, 2026
Stability Selection PFER Bound (Theorem 1) and Its Proof
Oct 13, 2026
Stability Selection Setup - Structure Estimation with Lasso and Graphical Lasso
Oct 13, 2026
Stability Selection Simulation Study - Designs A-G and Results
Oct 13, 2026
Stability Selection for Gaussian Graphical Models and Computational Cost
Oct 13, 2026
Conditional Permutation Importance (Strobl 2008 Algorithm)
Oct 13, 2026
Strobl 2008 - Correlated Predictor Selection in Tree Ensembles
Oct 13, 2026
Strobl 2008 - Digitized Figure Data for Selection Rates and Importance
Oct 13, 2026
Strobl 2008 - Discussion, mtry Guidance and Limitations
Oct 13, 2026
Strobl 2008 - Marginal vs Conditional Importance and Spurious Correlation
Oct 13, 2026
Strobl 2008 - Peptide-Binding Application
Oct 13, 2026
Strobl 2008 - Permutation Importance Null Hypotheses
Oct 13, 2026
Strobl 2008 - Simulation Results for Conditional Importance
Oct 13, 2026
Strobl et al 2008 - Conditional Variable Importance - Overview
Oct 13, 2026
NIST AI RMF - AI Lifecycle, AI Actors and TEVV
Oct 13, 2026
NIST AI RMF - Accountability, Explainability, Privacy and Fairness
Oct 13, 2026
NIST AI RMF - Applying the Framework to Marketing Analytics Models
Oct 13, 2026
NIST AI RMF - Core Structure and the GOVERN Function
Oct 13, 2026
NIST AI RMF - Framing AI Risk and Measurement Challenges
Oct 13, 2026
NIST AI RMF - How AI Risks Differ and Human-AI Interaction
Oct 13, 2026
NIST AI RMF - MANAGE Function
Oct 13, 2026
NIST AI RMF - MAP Function
Oct 13, 2026
NIST AI RMF - MEASURE Function
Oct 13, 2026
NIST AI RMF - Profiles, Effectiveness and Framework Attributes
Oct 13, 2026
NIST AI RMF - Trustworthy AI Characteristics, Tradeoffs and Validity
Oct 13, 2026
NIST AI RMF 1.0 - Overview
Oct 13, 2026
SR 11-7 - Applying Model Risk Management to Marketing Analytics
Oct 13, 2026
SR 11-7 - Conceptual Soundness, Sensitivity Analysis and Stress Testing
Oct 13, 2026
SR 11-7 - Governance Roles, Policies, Internal Audit and External Resources
Oct 13, 2026
SR 11-7 - Model Definition, Model Risk and Effective Challenge
Oct 13, 2026
SR 11-7 - Model Development, Implementation and Use
Oct 13, 2026
SR 11-7 - Model Inventory and Documentation Standards
Oct 13, 2026
SR 11-7 - Model Risk Management Guidance - Overview
Oct 13, 2026
SR 11-7 - Ongoing Monitoring, Process Verification, Overrides and Benchmarking
Oct 13, 2026
SR 11-7 - Outcomes Analysis and Back-Testing
Oct 13, 2026
SR 11-7 - Validation Independence, Validation Gate and Periodic Review
Oct 13, 2026
SR 11-7 - Validation of Vendor and Third-Party Models
Oct 13, 2026
Breiman 2001 - Bellman, Blessing of Dimensionality and Support Vector Machines
Oct 13, 2026
Breiman 2001 - Consulting Projects and Perceptions on Statistical Analysis
Oct 13, 2026
Breiman 2001 - Critique of Data Models - Goodness of Fit, Multiplicity and Predictive Accuracy
Oct 13, 2026
Breiman 2001 - Data Modeling vs Algorithmic Modeling Cultures
Oct 13, 2026
Breiman 2001 - Discussion by Cox, Efron, Hoadley and Parzen and Rejoinder
Oct 13, 2026
Breiman 2001 - Final Remarks and Glossary of Algorithmic Terms
Oct 13, 2026
Breiman 2001 - Information from a Black Box - Hepatitis, Liver and Microarray Examples
Oct 13, 2026
Breiman 2001 - Occam Dilemma and Random Forests vs Single Trees
Oct 13, 2026
Breiman 2001 - Rashomon Effect and the Multiplicity of Good Models
Oct 13, 2026
Breiman 2001 - Statistical Modeling The Two Cultures - Overview
Oct 13, 2026
Ioannidis 2005 - Bias Parameter u and Reverse Bias
Oct 13, 2026
Ioannidis 2005 - Box 1 Genome-Wide Association Worked Example
Oct 13, 2026
Ioannidis 2005 - Multiple Independent Teams and PPV Erosion
Oct 13, 2026
Ioannidis 2005 - Null Fields and Claimed Effects as Measures of Bias
Oct 13, 2026
Ioannidis 2005 - PPV of Research Findings and the 2x2 Framework
Oct 13, 2026
Ioannidis 2005 - Reference List and Citation Map
Oct 13, 2026
Ioannidis 2005 - Remedies, Limitations and Later Critiques
Oct 13, 2026
Ioannidis 2005 - Six Corollaries and the Proteus Phenomenon
Oct 13, 2026
Ioannidis 2005 - Table 4 PPV by Study Design
Oct 13, 2026
Riley 2020 - Anticipating the Cox-Snell R2 and Its Maximum
Oct 13, 2026
Riley 2020 - Beyond the 10 Events-per-Variable Rule
Oct 13, 2026
Riley 2020 - Extensions - Subgroups, Variable Selection, ML and Model Updating
Oct 13, 2026
Riley 2020 - Four-Step Procedure, pmsampsize and Worked Examples
Oct 13, 2026
Riley 2020 - Precision Criteria for Overall Risk, MAPE and Residual SD
Oct 13, 2026
Riley 2020 - References, Supplements and Back Matter
Oct 13, 2026
Riley 2020 - Shrinkage and Optimism Criteria for Overfitting
Oct 13, 2026
Riley et al 2020 - Sample Size for Prediction Models - Overview
Oct 13, 2026
Simmons 2011 - Ambiguity, Motivated Reasoning and the Cost of False Positives
Oct 13, 2026
Simmons 2011 - Chronological Rejuvenation Demonstration Studies
Oct 13, 2026
Simmons 2011 - Criticisms, Nonsolutions and Concluding Remarks
Oct 13, 2026
Simmons 2011 - Optional Stopping Simulations (Figures 1-2)
Oct 13, 2026
Simmons 2011 - Six Disclosure Requirements and Four Reviewer Guidelines
Oct 13, 2026
Simmons 2011 - Table 1 Simulations of Four Researcher Degrees of Freedom
Oct 13, 2026
Simmons Nelson Simonsohn 2011 - False-Positive Psychology - Overview
Oct 13, 2026
Chan Perry 2017 - Challenges in Media Mix Modeling - Overview
Oct 13, 2026
Chan Perry 2017 - Five Plausible MMMs (Model Selection and Model Uncertainty)
Oct 13, 2026
Chan Perry 2017 - MMM Data Limitations (156 Rows, Correlated Channels, Narrow Spend Range)
Oct 13, 2026
Chan Perry 2017 - MMM Data Sources and the Additive Regression Specification
Oct 13, 2026
Chan Perry 2017 - Opportunities (Better Data, Bayesian, Category, Geo, Controls, Graphs)
Oct 13, 2026
Chan Perry 2017 - Simulation-Based MMM Validation and Transparency
Oct 13, 2026
Chan Perry 2017 - Why Experiments and Matching Fall Short for MMM Questions
Oct 13, 2026
Morris et al 2019 - ADEMP Planning of Aims, DGMs, Estimands and Methods
Oct 13, 2026
Morris et al 2019 - Coding Simulations with Seeds, RNG States and Streams
Oct 13, 2026
Morris et al 2019 - Estimates Data Checks, n_sim Choice and Analysis Remarks
Oct 13, 2026
Morris et al 2019 - Full Reference List and Further Reading
Oct 13, 2026
Morris et al 2019 - Motivation and Review of Simulation Practice
Oct 13, 2026
Morris et al 2019 - Performance Measures and Monte Carlo Standard Errors
Oct 13, 2026
Morris et al 2019 - Reporting, Tables and Zip and Lollipop Plots
Oct 13, 2026
Morris et al 2019 - Worked Example Exponential vs Weibull vs Cox
Oct 13, 2026
AMSS Discussion, Limitations and Validation Practice
Oct 13, 2026
AMSS Ground Truth - Counterfactual Scenarios and Simulated ROAS
Oct 13, 2026
AMSS Hidden-State Consumer Segmentation and Migration Formalism
Oct 13, 2026
AMSS MMM Evaluation - Simple Scenario and Lagged-Effects Study
Oct 13, 2026
AMSS Motivation - Virtual Experiments for Marketing Measurement
Oct 13, 2026
AMSS Natural Migration, Satiation and Lagged-Effect Mechanics
Oct 13, 2026
AMSS Paid Search Module (Queries, Auction, Organic vs Paid)
Oct 13, 2026
AMSS Traditional Media Module (Reach, Frequency, Hill Effect)
Oct 13, 2026
Zhang Vaver 2017 - Aggregate Marketing System Simulator - Overview
Oct 13, 2026
Bergmeir 2018 - Conclusions, Decision Rule and Scope Limits
Oct 13, 2026
Bergmeir 2018 - Lag-Embedded K-Fold CV vs h-Block CV
Oct 13, 2026
Bergmeir 2018 - Monte Carlo Design and MAPAE-MPAE Metrics
Oct 13, 2026
Bergmeir 2018 - Monte Carlo Results for AR and MLP Forecasters
Oct 13, 2026
Bergmeir 2018 - Proof of Theorem 1 and Lemma 1
Oct 13, 2026
Bergmeir 2018 - Sunspot Example with Ljung-Box Residual Filter
Oct 13, 2026
Bergmeir 2018 - Theorem 1 Validity of K-Fold CV for Autoregressive Models
Oct 13, 2026
Bergmeir Hyndman Koo 2018 - CV for Time Series - Overview
Oct 13, 2026
Out-of-Sample Validation for Time-Series Models
Oct 13, 2026
Quantum Field Theory - Overview
Oct 13, 2026
Gaussian Distributions and Algebra
Oct 13, 2026
Activity Bias in Advertising
Oct 13, 2026
Forking Paths and Bayesian Approaches
Oct 13, 2026
Gelman Loken 2013 - Data Exclusion, Coding and Pooling Forks in the Red-Pink Study
Oct 13, 2026
Gelman Loken 2013 - Four Testing Procedures and the Data-Contingent Decision Variable
Oct 13, 2026
Gelman Loken 2013 - Front Matter, Borges Epigraph and References
Oct 13, 2026
Gelman Loken 2013 - Garden of Forking Paths - Overview
Oct 13, 2026
Gelman Loken 2013 - Main Effects vs Interactions Case Studies
Oct 13, 2026
Gelman Loken 2013 - Three Reactions and the Bayesian Reading of Bem's 53 Percent
Oct 13, 2026
Gelman Loken 2013 - Way Forward - Preregistration, Replication and Analyzing All Comparisons
Oct 13, 2026
Multiple Comparisons - Bayesian Perspective
Oct 13, 2026
Nuisance Parameter Bias Simulation
Oct 13, 2026
Observational vs Experimental Methods in Advertising
Oct 13, 2026
Limits and Objections to Pre-registration
Oct 13, 2026
Pre-analysis Plans and the Open Science Ecosystem
Oct 13, 2026
Pre-registration and Open Science - Overview
Oct 13, 2026
Table 2 Fallacy
Sep 23, 2026
Bayesian Copula Estimation
Sep 23, 2026
LLM Expert Elicitation for Bayesian Networks
Sep 23, 2026
LLM-BN Decision Support Application
Sep 23, 2026
Shaposhnyk 2025 - Overview
Sep 23, 2026
Counterfactual Inference
Sep 23, 2026
Missing Data — Statistical Rethinking (Lecture 18)
Sep 23, 2026
Bayesian Moderation Analysis
Sep 23, 2026
Synthetic Likelihood Construction
Sep 23, 2026
Bayesian Workflow - Overview
Sep 23, 2026
Choosing and Building Models
Sep 23, 2026
Computational Troubleshooting
Sep 23, 2026
Evaluating Fitted Models
Sep 23, 2026
Fitting and Validating Computation
Sep 23, 2026
Iterative Model Improvement
Sep 23, 2026
Basic Category Theory - Overview
Sep 23, 2026
Adjunctions Induce Monads
Sep 23, 2026
Algebras for a Monad - Eilenberg-Moore and Kleisli
Sep 23, 2026
Beck's Monadicity Theorem
Sep 23, 2026
Monads - Overview
Sep 23, 2026
Monads and the Monad Laws
Sep 23, 2026
Dependence Measures for Copulas
Sep 23, 2026
SMM Copula Asymptotic Theory
Sep 23, 2026
SMM Copula Simulation and Application
Sep 23, 2026
SMM Copula Specification Testing
Sep 23, 2026
SMM Estimator for Copulas
Sep 23, 2026
Efficient Method of Moments
Sep 23, 2026
Indirect Inference
Sep 23, 2026
Method of Simulated Moments
Sep 23, 2026
Practical Issues in Simulation Estimation
Sep 23, 2026
Simulation-Based Estimation - Overview
Sep 23, 2026
Bayesian Difference in Differences
Sep 23, 2026
Quasi-Bayes for Plausible Moment Restrictions
Sep 23, 2026
Probabilistic Numerics - Overview
Sep 18, 2026
ABM Calibration Case Studies
Sep 18, 2026
ABM Calibration Overview
Sep 18, 2026
Approximate Bayesian Computation for ABMs
Sep 18, 2026
GA Fitness Evaluation and the RAM
Sep 18, 2026
Genetic Algorithm Calibration for ABM
Sep 18, 2026
HM-ABC Calibration Framework
Sep 18, 2026
History Matching for ABMs
Sep 18, 2026
Uncertainty Quantification for ABM Calibration
Sep 18, 2026
ABM Validation Challenges
Sep 18, 2026
Karakaya et al 2011 - Overview
Sep 18, 2026
Logit Purchase Decision Model
Sep 18, 2026
Heterogeneity in Agent Models
Sep 18, 2026
Agent Decision Rules and Bounded Rationality
Sep 18, 2026
Lindley's Information Measure
Sep 18, 2026
Nested Estimation and Nested Monte Carlo
Sep 18, 2026
Sequential and Adaptive BED
Sep 18, 2026
Adaptive Contrastive Estimation (ACE)
Sep 18, 2026
High-Dimensional Design Applications
Sep 18, 2026
Likelihood-Free ACE and Gradient Estimation
Sep 18, 2026
Prior Contrastive Estimation (PCE)
Sep 18, 2026
From Designs to Policies (Deep Adaptive Design)
Sep 18, 2026
Open Challenges and Future Directions
Sep 18, 2026
Approximate Thompson Sampling and Practical Extensions
Sep 18, 2026
Bernoulli Bandit and Thompson Sampling Algorithm
Sep 18, 2026
Contextual and Linear Bandits
Sep 18, 2026
Multi-Armed Bandits and Thompson Sampling - Overview
Sep 18, 2026
Regret Bounds for Thompson Sampling
Sep 18, 2026
UCB and Greedy Algorithms for Bandits
Sep 18, 2026
Convergence Rates and Estimator Selection
Sep 18, 2026
Implicit Likelihood Estimator
Sep 18, 2026
Variational Marginal Estimator
Sep 18, 2026
Variational NMC Estimator
Sep 18, 2026
Variational Posterior Estimator (Barber-Agakov)
Sep 18, 2026
Bayesian Outcome Models
Sep 18, 2026
General Structure of Bayesian Causal Inference
Sep 18, 2026
A-learning and Robustness
Sep 18, 2026
Dynamic Treatment Regimes Framework
Sep 18, 2026
Optimal Regime via Dynamic Programming
Sep 18, 2026
Q-learning
Sep 18, 2026
Frequentist Causal Estimation
Sep 18, 2026
Instrumental Variables and Principal Stratification
Sep 18, 2026
Time-Varying Treatments and G-computation
Sep 18, 2026
CausalImpact Empirical Application
Sep 18, 2026
Counterfactual Impact Estimation
Sep 18, 2026
Local Linear Trend and Seasonality
Sep 18, 2026
MCMC Inference for CausalImpact
Sep 18, 2026
State-Space Models and the Kalman Filter - Overview
Sep 18, 2026
The RTS Smoother
Sep 18, 2026
Metalearner Simulation Results
Sep 18, 2026
S-Learner
Sep 18, 2026
T-Learner and Minimax Rate
Sep 18, 2026
X-Learner
Sep 18, 2026
Asymptotics and Frequentist Connections
Sep 18, 2026
Data Collection Models
Sep 18, 2026
Modeled and Unmodeled Data
Sep 18, 2026
Prior Distributions
Sep 18, 2026
Relating a Model to Subject-Matter Assumptions
Sep 18, 2026
Simulation to Express Uncertainty
Sep 18, 2026
Tail Behavior and Prior-Likelihood Conflict
Sep 18, 2026
Building Up to a Hierarchical Model - Coronavirus Testing
Sep 18, 2026
Model Building - Time-Series Decomposition for Birthdays
Sep 18, 2026
Modeling Ideas to Address Computing Problems
Sep 18, 2026
Simulation-Based and Amortized Inference
Sep 18, 2026
Variational Inference and Pathfinder
Sep 18, 2026
Data-Averaged Posterior Self-Consistency
Sep 18, 2026
Comparing Models Visually
Sep 18, 2026
Influence of Individual Data Points
Sep 18, 2026
Topology of Models
Sep 18, 2026
Varieties of Bayesian Theory
Sep 18, 2026
Interpreting SBC Histograms
Sep 18, 2026
Rank Statistics and Uniformity
Sep 18, 2026
Group-Time Average Treatment Effects
Sep 18, 2026
Simultaneous Inference via Multiplier Bootstrap
Sep 18, 2026
SMM Weighting Matrix and Inference
Sep 18, 2026
Standard Errors and Clustering
Sep 18, 2026
The Selection Problem
Sep 18, 2026
Differences-in-Differences
Sep 18, 2026
Generalized Synthetic Control Method
Sep 18, 2026
Instrumental Variables
Sep 18, 2026
Local Average Treatment Effects
Sep 18, 2026
Synthetic Control Bias Theory
Sep 18, 2026
Synthetic Control Extensions
Sep 18, 2026
Synthetic Control Inference and Diagnostics
Sep 18, 2026
Synthetic Control Requirements
Sep 18, 2026
Synthetic Control
Sep 18, 2026
Gaussian Local Prior Approximation
Sep 18, 2026
Plausible GMM - Institutions and GDP Application
Sep 18, 2026
Plausible GMM - Overview
Sep 18, 2026
Plausible Moment Restriction Model
Sep 18, 2026
Regression and the Conditional Expectation Function
Sep 18, 2026
Time-Based Regression Estimator for Geo Experiments
Sep 18, 2026
Further Topics in ODE Solvers
Sep 18, 2026
Solving ODEs as Inference
Sep 18, 2026
Bayesian Filtering and Smoothing
Sep 18, 2026
Gauss-Markov Processes and SDEs
Sep 18, 2026
Hierarchical Inference in Gaussian Models
Sep 18, 2026
Value Loss and Entropy Search
Sep 18, 2026
Bandit Models with Delayed and Censored Feedback
Sep 18, 2026
Delayed Feedback Model for Conversion Prediction
Sep 18, 2026
Delayed and Censored Feedback - Overview
Sep 18, 2026
EM and Gradient Optimization for the Delayed Feedback Model
Sep 18, 2026
Logic of Regression Adjustment
Sep 18, 2026
Prediction vs Postdiction
Sep 18, 2026
Within-Between Persons Causal Inference
Sep 18, 2026
Ben Said et al 2002 - Overview
Sep 18, 2026
ABM Methodology and Principles
Sep 18, 2026
Emergent Phenomena in ABM
Sep 18, 2026
Opinion Leaders and Social Influence
Sep 18, 2026
Word of Mouth Mechanisms
Sep 18, 2026
Code Prompts for Causal Structure: Encoding Causal Graphs with Conditional Statements
Sep 18, 2026
Fine-tuning on Conditional Statements: Improving LLM Causal Reasoning via Code Training
Sep 18, 2026
LLM Causal Reasoning Tasks: Abductive NLG and Counterfactual Reasoning
Sep 18, 2026
Li et al. 2022 - Bayesian Causal Inference: A Critical Review
Sep 18, 2026
Approximation Methods
Sep 18, 2026
Introduction to Bayesian Computation
Sep 18, 2026
MCMC Basics
Sep 18, 2026
Probability and Bayesian Inference
Sep 18, 2026
Single-Parameter Models
Sep 18, 2026
Model Comparison
Sep 18, 2026
Bayesian Workflow Book - Overview
Sep 18, 2026
Models for Regression Coefficients and Variable Selection - Student Grades
Sep 18, 2026
Approximations Based on Joint and Conditional Posterior Modes
Sep 18, 2026
Model Expansion - Predictive Consistency and Coherence
Sep 18, 2026
Stacking and Predictive Model Averaging
Sep 18, 2026
From Inference to Data Analysis to Workflow
Sep 18, 2026
SBC Case Studies
Sep 18, 2026
Adjunctions via Initial Objects
Sep 18, 2026
Pullbacks
Sep 18, 2026
Markov and Faithfulness Assumptions
Sep 18, 2026
Aggregating Group-Time Effects
Sep 18, 2026
Identifying Assumptions for Staggered DiD
Sep 18, 2026
Covariate Balance and Matching Diagnostics
Sep 18, 2026
Matching Algorithms and Caliper
Sep 18, 2026
Quantum Entanglement
Sep 18, 2026
Quantum Mechanics - Key Phenomena
Sep 18, 2026
Quantum Mechanics - Mathematical Formalism
Sep 18, 2026
Quantum Mechanics - Overview
Sep 18, 2026
Schrödinger Equation and Time Evolution
Sep 18, 2026
Wave Function and Hilbert Space
Sep 18, 2026
Canonical Quantization of Fields
Sep 18, 2026
Gauge Theory - Overview
Sep 18, 2026
QED and Renormalization
Sep 18, 2026
Renormalization
Sep 18, 2026
Standard Model and Gauge Groups
Sep 18, 2026
Yang–Mills Theory and Gauge Fields
Sep 18, 2026
Bayesian Quadrature
Sep 18, 2026
Fixed-Effects Model
Sep 18, 2026
Adjoint Functor Theorems
Sep 18, 2026
Cartesian Closed Categories
Sep 18, 2026
SMM Estimation of Factor Copulas
Aug 18, 2026
How to Get the Most Out of Bayesian Data Analysis
Aug 18, 2026
Statistical and Computational Workflow for Bayesians and Non-Bayesians
Aug 18, 2026
A Data Model Is Not Just a Likelihood
Aug 18, 2026
Causal Inference as Generalization
Aug 18, 2026
Choosing an Initial Model
Aug 18, 2026
Expressing a Bayesian Model with Probability Distributions
Aug 18, 2026
Generative and Partially Generative Models
Aug 18, 2026
Point Estimates and Uncertainties
Aug 18, 2026
Simulating an Underlying Process, Data Collection, and Inference
Aug 18, 2026
Specifying the Data Model and the Prior
Aug 18, 2026
Coding a Series of Models - Movie Ratings
Aug 18, 2026
Debugging a Model - World Cup Football
Aug 18, 2026
Incremental Development and Testing - Black Cat Adoptions
Aug 18, 2026
LOO Model Checking and Comparison - Roaches
Aug 18, 2026
Model Building and Expansion - Golf Putting
Aug 18, 2026
Posterior Predictive Checking - Stochastic Learning in Dogs
Aug 18, 2026
Predictive Model Checking and Comparison - Clinical Trial
Aug 18, 2026
Prior Specification for Regression Models - Sleep Study
Aug 18, 2026
Simulation-Based Calibration Checking in Model Development Workflow
Aug 18, 2026
Using a Fitted Model for Decision Analysis - Classification Competition
Aug 18, 2026
Approximate Algorithms and Approximate Models
Aug 18, 2026
Chains, Iterations, and Effective Sample Size
Aug 18, 2026
Divide-and-Conquer Algorithms
Aug 18, 2026
Failure Modes and Steps Forward
Aug 18, 2026
Fit Fast, Fail Fast
Aug 18, 2026
Fitting Simpler Models for Computational Purposes
Aug 18, 2026
Initial Values, Adaptation, and Warmup
Aug 18, 2026
Statistical Modeling as Software Development
Aug 18, 2026
Big Data Need Big Models
Aug 18, 2026
Simulated-Data Experimentation as Virtual Replication
Aug 18, 2026
Software Assisted Workflow
Aug 18, 2026
Statistical and Scientific Inference
Aug 18, 2026
Visualizing High-Dimensional Inference
Aug 18, 2026
Bioassay - A First Probabilistic Program
Aug 18, 2026
Computational Tools and Probabilistic Programming
Aug 18, 2026
Four Modeling Scenarios
Aug 18, 2026
Multiple-Choice Exam - A Full Workflow Walkthrough
Aug 18, 2026
Why Bayes - Benefits, Costs, and Borders
Aug 18, 2026
SBC in the Workflow
Jul 01, 2026
Bayesian Data Analysis 3rd Edition - Overview
Jul 01, 2026
Statistical Rethinking: A Bayesian Course with Examples in R and Stan
Jul 01, 2026
Mostly Harmless Econometrics: An Empiricist's Companion
Jul 01, 2026
Classical Quadrature as Inference
Jul 01, 2026
First- and Second-Order Optimisation Methods
Jul 01, 2026
Probabilistic Step-Size Selection and Line Searches
Jul 01, 2026
The Global Optimisation Problem
Jul 01, 2026
Classical ODE Solvers as Regression
Jul 01, 2026
Computation as Probabilistic Inference
Jul 01, 2026
The Numerical Agent
Jul 01, 2026
Active Bayesian Quadrature and Bayesian Monte Carlo
Jul 01, 2026
Convergence and Priors in Bayesian Quadrature
Jul 01, 2026
Kernel Quadrature and Kernel Means
Jul 01, 2026
Lessons from Integration
Jul 01, 2026
The Integration Problem
Jul 01, 2026
Further Topics in Global Optimisation
Jul 01, 2026
The Local Optimisation Problem
Jun 28, 2026
Population Initialization and Parameter Sensitivity
Jun 28, 2026
Summary Causal DAGs: Definition, Node Contraction, and NP-Hardness
Jun 28, 2026
BN Construction Methods Comparison
Jun 28, 2026
Regularized Horseshoe (Finnish Horseshoe)
Jun 28, 2026
The Horseshoe Prior
Jun 28, 2026
Adjoint Functors
Jun 28, 2026
Units and Counits
Jun 28, 2026
Functional Causal Models (LiNGAM, ANM)
Jun 28, 2026
PC Algorithm and Constraint-Based Discovery
Jun 28, 2026
NOTEARS - Overview
Jun 28, 2026
C-vines, D-vines, and Regular Vines
Jun 28, 2026
Estimation and Structure Selection for Vines
Jun 28, 2026
Pair-Copula Constructions
Jun 28, 2026
The Simplifying Assumption
Jun 28, 2026
Vine Copulas - Overview
Jun 28, 2026
Pre-registration vs Registered Reports
Jun 27, 2026
Bayesian Experimental Design - Overview
Jun 27, 2026
Unified SGD BOED - Overview
Jun 27, 2026
Information-Theoretic Design Objectives
Jun 27, 2026
Modern Bayesian Experimental Design - Overview
Jun 27, 2026
Optimization and Gradient Schemes for BED
Jun 27, 2026
The Computational Revolution in EIG Estimation
Jun 27, 2026
Variational BOED - Overview
Jun 27, 2026
Nicholson's Blowfly Application
Jun 27, 2026
Q- and A-learning - Overview
Jun 27, 2026
CaGReS Algorithm: Greedy Causal DAG Summarization
Jun 27, 2026
Zeng 2025 - Overview: Causal DAG Summarization
Jun 27, 2026
Bayesian Linear Regression
Jun 27, 2026
Difference-in-Differences with Multiple Time Periods - Overview
Jun 27, 2026
Duffie-Singleton Asset-Pricing Model
Jun 27, 2026
Geometric Ergodicity and Uniform LLN
Jun 27, 2026
SME Asymptotic Distribution
Jun 27, 2026
SME Consistency
Jun 27, 2026
SME Extensions and Applications
Jun 27, 2026
Simulated Moments Estimation - Overview
Jun 27, 2026
Simulated Moments Estimator Definition
Jun 27, 2026
NOTEARS Algorithm
Jun 27, 2026
Multi-Factor and Block Dependence Structures
Jun 17, 2026
DAG Structure Learning Problem
Jun 17, 2026
NOTEARS Experiments
Jun 17, 2026
Smooth Characterization of Acyclicity
Jun 17, 2026
Factor Copula Application - S&P 100 and Systemic Risk
Jun 17, 2026
Factor Copula Construction
Jun 17, 2026
Tail Dependence in Factor Copulas
Jun 17, 2026
Behavioral Attitudes in CUBES
Jun 17, 2026
s-Separation in Summary DAGs: Sound and Complete CI Identification
Jun 17, 2026
Causal Model - Cause Precondition Effect
Jun 17, 2026
Brodersen 2015 - Overview
Jun 17, 2026
Functors and Limits
Jun 17, 2026
Products and Equalizers
Jun 17, 2026
Brock-Mirman Model - SMM Estimation Exercise
Jun 17, 2026
Uncertainty Principle
Jun 17, 2026
Within-Between Persons Distinction - Overview
May 08, 2026
Categories
May 08, 2026
Functor Categories
May 08, 2026
Functors
May 08, 2026
Natural Transformations
May 08, 2026
Colimits
May 08, 2026
General Limits
May 08, 2026
Representable Functors
May 08, 2026
Yoneda Embedding and Consequences
May 08, 2026
Yoneda Lemma
May 08, 2026
Adjoints and Limits
May 08, 2026
Limits in Presheaf Categories
May 08, 2026
Limits via Representables
May 08, 2026
Universal Properties - Introduction
Apr 13, 2026
Organizational Simulation
Apr 13, 2026
CUBES Simulator Architecture
Apr 12, 2026
Consumer Utility Function Components
Apr 12, 2026
ABM vs Equation-Based Modeling
Apr 12, 2026
Product Adoption and Diffusion Models
Apr 12, 2026
Multiparameter Models
Apr 12, 2026
Posterior Sampling and Summarization
Apr 12, 2026
Model Checking
Apr 12, 2026
Linear Models in Statistical Rethinking
Apr 12, 2026
SMM Python Implementation
Apr 12, 2026
Canonical Causal DAGs: Node Contraction as Edge Addition
Apr 12, 2026
Statistical Rethinking: The Golem of Prague
Apr 12, 2026
Xu 2016 - Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models
Apr 12, 2026
Entropy-Based BN Evaluation
Apr 12, 2026
Cross-Lagged and Dynamic Panel Models
Apr 11, 2026
Estimands in Longitudinal Research
Apr 11, 2026
Research Questions in Econometrics
Apr 11, 2026
Flow Simulation Applications
Apr 11, 2026
Market and Financial Simulation
Apr 11, 2026
Operational Risk Modeling with ABM
Apr 11, 2026
Behavioral Primitives and Thresholds
Apr 11, 2026
Imitation and Conditioning Processes
Apr 11, 2026
Market Share Equilibrium and Lock-In
Apr 11, 2026
Network Topology Effects on Diffusion
Apr 11, 2026
Social Network Formation in Consumer Markets
Apr 11, 2026
Code Prompt Aspects Analysis: What Makes Code Prompts Effective for Causal Reasoning
Apr 11, 2026
Code vs Text Prompt Evaluation: LLM Causal Reasoning Benchmarks
Apr 11, 2026
Interactive Knowledge Elicitation Method
Apr 11, 2026
Liu 2025 - Overview: Eliciting and Improving Causal Reasoning in LLMs with Conditional Statements
Apr 11, 2026
NLP Causal Extraction Methods
Apr 11, 2026
Yamashita 2020 - Overview
Apr 11, 2026
Künzel 2019 - Overview
Apr 11, 2026
Missing Data Models
Apr 11, 2026
Abadie 2021 - Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects

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