Second Brain

Tag: doc/textbook

202 items with this tag.

Sep 23, 2026
Efficient MCMC
Sep 23, 2026
HMC and Stan in Practice
Sep 23, 2026
Hierarchical Linear Models
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
Modeling as Software Development
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
Discrete Choice Models
Sep 23, 2026
The Experimental Ideal
Sep 23, 2026
Bayesian Difference in Differences
Sep 23, 2026
Probabilistic Numerics - Overview
Sep 18, 2026
Linear-Gaussian State-Space Models
Sep 18, 2026
Marginal Likelihood via the Kalman Filter
Sep 18, 2026
State-Space Models and the Kalman Filter - Overview
Sep 18, 2026
The Kalman Filter
Sep 18, 2026
The RTS Smoother
Sep 18, 2026
Asymptotics and Frequentist Connections
Sep 18, 2026
Empirical Bayes - Overview
Sep 18, 2026
Empirical Bayes Interpretation of Shrinkage
Sep 18, 2026
James-Stein Estimator
Sep 18, 2026
Robbins Formula and Poisson Empirical Bayes
Sep 18, 2026
Stein's Paradox and Risk Dominance
Sep 18, 2026
Hierarchical Models
Sep 18, 2026
Data Collection Models
Sep 18, 2026
Decision Analysis
Sep 18, 2026
Overfitting and Information Criteria
Sep 18, 2026
Spurious Association and Confounds
Sep 18, 2026
Constructing Priors for Effect Sizes
Sep 18, 2026
From Inference to Decision
Sep 18, 2026
Modeled and Unmodeled Data
Sep 18, 2026
Poststratification
Sep 18, 2026
Prior Distributions
Sep 18, 2026
Prior Predictive Checking
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
Comparing Models Visually
Sep 18, 2026
Cross Validation Checking
Sep 18, 2026
Influence of Individual Data Points
Sep 18, 2026
Influence of Likelihood and Prior
Sep 18, 2026
Model Selection and Overfitting
Sep 18, 2026
Posterior Predictive Checking
Sep 18, 2026
Topology of Models
Sep 18, 2026
Varieties of Bayesian Theory
Sep 18, 2026
Standard Errors and Clustering
Sep 18, 2026
The Selection Problem
Sep 18, 2026
Differences-in-Differences
Sep 18, 2026
Instrumental Variables
Sep 18, 2026
Local Average Treatment Effects
Sep 18, 2026
Conditional Independence Assumption
Sep 18, 2026
Omitted Variables Bias
Sep 18, 2026
Regression and the Conditional Expectation Function
Sep 18, 2026
Further Topics in ODE Solvers
Sep 18, 2026
ODE Filters and Smoothers
Sep 18, 2026
Solving ODEs as Inference
Sep 18, 2026
Theory of ODE Filters and Smoothers
Sep 18, 2026
Bayesian Filtering and Smoothing
Sep 18, 2026
Gauss-Markov Processes and SDEs
Sep 18, 2026
Gaussian Process Regression
Sep 18, 2026
Hierarchical Inference in Gaussian Models
Sep 18, 2026
Uncertainty Calibration for Linear Solvers
Sep 18, 2026
Acquisition Functions
Sep 18, 2026
Bayesian Optimisation
Sep 18, 2026
Value Loss and Entropy Search
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
Generalized Linear Models
Sep 18, 2026
Bayesian Workflow Book - Overview
Sep 18, 2026
Designing Simulated-Data Experiments
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
Model Selection Using Predictive Performance
Sep 18, 2026
Stacking and Predictive Model Averaging
Sep 18, 2026
From Inference to Data Analysis to Workflow
Sep 18, 2026
There Is No Safe Haven
Sep 18, 2026
Adjunctions via Initial Objects
Sep 18, 2026
Pullbacks
Sep 18, 2026
Quantile Regression
Sep 18, 2026
Bayesian Quadrature
Sep 18, 2026
Adjoint Functor Theorems
Sep 18, 2026
Cartesian Closed Categories
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
Joint Priors and Covariance Matrices
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
Challenge of Multimodality - Differential Equation for Planetary Motion
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
Model Building with Latent Variables - Animal Movement
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
Sampling Problems with Latent Variables - No Vehicles in the Park
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
Effective Sample Size and Monte Carlo Standard Error
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
How Many Digits to Report
Aug 18, 2026
Initial Values, Adaptation, and Warmup
Aug 18, 2026
Statistical Modeling as Software Development
Aug 18, 2026
The Typical Set and the Log Posterior Density
Aug 18, 2026
What to Do About Convergence Problems
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
The Replication Crisis and Multiple Levels of Variation
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
Perturbative ODE Solvers
Jul 01, 2026
Computation as Probabilistic Inference
Jul 01, 2026
Gaussian Distributions and Algebra
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
Classic Linear Solvers - A Review
Jul 01, 2026
Computational Constraints on Probabilistic Solvers
Jul 01, 2026
Conjugate Gradients as Probabilistic Inference
Jul 01, 2026
Gaussian Priors over Matrices and the Symmetric Kronecker Product
Jul 01, 2026
Probabilistic Linear Solvers - Algorithmic Scaffold
Jul 01, 2026
The Linear Algebra Problem and Evaluation Strategies
Jul 01, 2026
Further Topics in Global Optimisation
Jul 01, 2026
The Local Optimisation Problem
Jun 28, 2026
Adjoint Functors
Jun 28, 2026
Units and Counits
Jun 27, 2026
Bayesian Linear Regression
Jun 17, 2026
Functors and Limits
Jun 17, 2026
Products and Equalizers
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
Garden of Forking Data
Apr 12, 2026
Monsters and Mixtures
Apr 12, 2026
Nonparametric Models Overview
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
Regression Discontinuity Designs
Apr 12, 2026
Statistical Rethinking: The Golem of Prague
Apr 11, 2026
Research Questions in Econometrics
Apr 11, 2026
Missing Data Models

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