Second Brain
About
A structured knowledge base covering Bayesian statistics and applied Bayesian model families, econometrics, causal inference and causal discovery, market response modeling, Bayesian experimental design, evidence synthesis and privacy, model validation and interpretability, machine learning and AI, probabilistic numerics, and agent-based modeling. Notes are cross-linked by topic — use the graph view or search bar to explore connections across the collection.
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Selected Analyses
The vault uses ‘calibration’ in at least four senses — simulation-based calibration of a Bayesian computation, coverage calibration in conformal prediction, probabilistic calibration of forecasts under proper scoring rules, and parameter calibration of agent-based models (plus uncertainty calibration of probabilistic numerical solvers and LLM-population calibration). What does each one guarantee, and what does it not?
September 18, 2026 · Calibration · Bayesian Workflow · Conformal Prediction · Uncertainty Quantification · Agent Based Modeling
Every use of “calibrated” in the vault is shorthand for a sentence with three blanks: this object’s stated uncertainty matches frequencies, averaged over this reference distribution, assuming this.
For one geo-test dataset, when would Time-Based Regression, CausalImpact (BSTS), synthetic control, generalized synthetic control, synthetic difference-in-differences and Bayesian DiD give different answers, and which assumption drives each difference?
September 18, 2026 · Causal Inference · Market Response · Geo Experiments · Synthetic Control · Difference In Differences
All six estimators impute the same missing block — the treated geos’ untreated outcomes during the test window — and differ only in what structure they assume the untreated panel has and how they are allowed to extrapolate from it.
calibration splits all the same cure for the garden of forking paths?
September 18, 2026 · Research Methodology · Multiple Comparisons · Machine Learning · Causal Inference · Bayesian Workflow
No — they are three different cures for one disease. The disease is : the analysis was chosen by the data that then evaluate it.
VNMC bounds on expected information gain, the contrastive PCE and ACE bounds, neural ratio estimation and the forward-KL objective of neural posterior estimation relate? Which direction of KL does each use, is each an upper or lower bound, and what failure does that choice cause?
September 18, 2026 · Variational Inference · Bayesian Experimental Design · Likelihood Free Inference · Bayesian Statistics
Every one of these objectives is the same identity, intractable quantity = computable surrogate ± an expected KL, obtained by replacing an intractable density with a learned .
ratio estimation?
September 18, 2026 · Agent Based Modeling · Likelihood Free Inference · Simulation Estimation · Calibration · Bayesian Statistics
The eleven methods differ on only three design choices: what is compared (hand-picked moments, an auxiliary model’s parameters, or learned summaries), how the comparison is scored (a quadratic distance to minimise, a threshold to pass, a Gaussian or neural density to evaluate) and whether the simulator is called inside the search loop or once up front.
Knowledge Base
| Domain | Core Topics |
|---|---|
| Bayesian Statistics | Inference fundamentals, hierarchical models, MCMC, model checking |
| Econometrics | Identification strategies, regression foundations, simulation-based estimation |
| Agent-Based Modeling | Calibration methods, social dynamics, consumer behavior |
| Research Methodology | Experimental design, multiple comparisons, causal reasoning |
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