How camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers works
This repository is an educational book and collection of runnable examples about Bayesian inference and probabilistic programming, not a single application. A reader follows chapter material from introductory models through MCMC, large-number reasoning, loss functions, and prior selection, with notebooks and supporting examples illustrating the ideas. The README documents those chapter topics; the source excerpts also show standalone modeling examples and small utilities, including a browser-based Bayesian bandit demonstration and competition metrics. Connections between chapter material and individual examples are not asserted where the excerpts do not show them.
Main entry points: Chapter1_Introduction Bayesian Introduction · Chapter2_MorePyMC Model Construction · Chapter3_MCMC MCMC Diagnostics · Chapter4_TheGreatestTheoremNeverTold Large Numbers
Components 13
Bayesian Foundations
- Bayesian Introduction
Chapter1_Introduction - Model Construction
Chapter2_MorePyMC
Inference Methods
- MCMC Diagnostics
Chapter3_MCMC - Large Numbers
Chapter4_TheGreatestTheoremNeverTold
Decision Applications
- Loss Functions
Chapter5_LossFunctions - Prior Selection
Chapter6_Priorities - Bayesian ML
Chapter7_BayesianMachineLearning
Runnable Examples
- Chapter Examples
ExamplesFromChapters - Bandit Demo
Chapter6_Priorities/d3bandits.js
Supporting Tools
- Dark Worlds Metric
Chapter5_LossFunctions/DarkWorldsMetric.py - Separation Plot
Chapter2_MorePyMC/separation_plot.py - AUC Metric
Chapter7_BayesianMachineLearning/auc.py
Other components
- Reader
Connections 7
- Reader → Bayesian Introduction: reads first
README.md - Reader → Model Construction: studies modeling
README.md - Reader → MCMC Diagnostics: studies sampling
README.md - Reader → Large Numbers: studies limits
README.md - Reader → Loss Functions: studies decisions
README.md - Reader → Prior Selection: studies priors
README.md - Reader → Bayesian ML: explores extension