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List of resources for bayesian inference

Awesome Bayes

List of resources for bayesian inference

Books

Software/packages

General inference

  • 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.
  • Zhusuan: Another probabilistic programming framework built on tensorflow.
  • Pyro: Probabilistic programming in Pytorch. Good docs, scalable models too.
  • Brancher: Probabilistic inference based on auto diff and variational models, also based on Pytorch.
  • LaplacesDemon: Mysterious probabilistic programming package in R with a cult following.
  • WebPPL: Probabilistic programming in the browser.
  • Turing.jl: Probabilistic programming in Julia, by Zoubin Ghahramani's lab.
  • Infer.NET: Specializes in running probabilistic inference in factor graphs (Expectation Propagation, Variational Inference). Programs written in .NET.

Specific

  • 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

Misc

  • 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.
  • StatSim: Browser-based interface to create, share, and perform inference on probabilistic models. Powered by WebPPL and PyMC3.

Resources, papers, and blogs

General topics

Introductory

MCMC

Variational Inference

Empirical Bayes

Non-parametrics

INLA

Bayesian deep learning

Misc

Michael Betancourt's case studies

Indexed here, these deserve a list all to themselves:

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