List of resources for bayesian inference
- Statistical Rethinking
- Bayesian Data Analysis
- Probability Theory: The Logic of Science
- The Bayesian Choice
- Bayesian methods for hackers
- Bayesian Analysis with Python | errata and extra material
- BUGS: Bayesian Inference Using Gibbs Sampling. Oldest of the Bayesian inference platforms, tried and tested. Has a Windows friendly version, WinBUGS. R, python and many other language bindings, GUIs and
- JAGS: Just another Gibbs sampler, similar to BUGS - focused on cross-platform, usability. Also tried and tested. R and python bindings too.
- Stan: Full-featured Bayesian inference with R and python bindings. Based on Hamiltonian MC and NUTS. Current favorite of the community it seems with lots of examples, docs.
- PyMC3: Probabilistic programming in Python/Theano. PyMC4 is in dev, will use Tensorflow as backend. Great API and interface, but hindered by Theano's deprecation. PYMC4 promises great things.
- edward2/tfprobability: Probabilistic programming in tensorflow. Scalable models, but little docs.
- Pyro: Probabilistic programming in Pytorch. Good docs, scalable models too.
- LaplacesDemon: Mysterious probabilistic programming package in R with a cult following.
- brms : Generalized linear/non-linear multilevel models, uses Stan.
- R-INLA : Latent Gaussian models via Integrated Nested Latent Approximations. Really fast compared to MCMC.
- bayesmix: Finite mixture models with JAGS in R
- lmm: Linear mixed models fitted with MCMC
- List of Bayesian inference packages for R: Comprehensive list for all Bayesian inference in R
- ArviZ: ArviZ is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, sample diagnostics, model checking, and comparison. Works with PyMC3, PyStan, emcee, Pyro and TensorFlow Probability.
Resources, papers, and blogs
- Count Bayesie: Will Kurt from "Get Programming with Haskell" fame explains basic probability and stats concepts through a Bayesian lens in a fun way.
- How to become a Bayesian in eight easy steps
- Introduction to Bayesian Statistics: Course lectures by Brendon Brewer (University of Auckland)
- Michael Jordan's Bayesian Statistics Course Notes
- The MCMC interactive gallery: Build intuition for common MCMC routines using interactive demos. A walkthrough of the demos can be found here.
- The MCMC handbook intro to MCMC: A no-frills intro to MCMC
- Scaling up Bayesian inference: For Big Data™
- Intro to variational inference via mean field approx
- David Blei's Variational Inference tutorial
- Expectation Maximization and Variational Inference
- Collection of tutorials on non-parametrics
- Infinite mixture models
- A Visual Exploration of Gaussian Processes
Bayesian deep learning
- Yarin Gal's talk on Bayesian Deep Learning: Blog post of the talk is also very informative, check it out here
- What is a variational autoencoder?
- Probabilistic Numerics: View all numeric optimization through a Bayesian lens
Michael Betancourt's case studies
Indexed here, these deserve a list all to themselves:
- Principled Bayesian Worflow: What to know and what to look for when doing Bayesian inference.
- Conceptual introduction to Hamiltonian MC
- Identifying Bayesian Mixture Models
- Diagnosing Biased Inference Using Divergences