Bayesian Analysis Recipes

A collection of Bayesian data analysis recipes using PyMC3
Alternatives To Bayesian Analysis Recipes
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
5 years ago
A list of resources for all invited talks, tutorials, workshops and presentations at NIPS 2017
Bayesian Analysis Recipes510
a year ago2mitJupyter Notebook
A collection of Bayesian data analysis recipes using PyMC3
Bayesiandeeplearning Survey436
2 months ago
Bayesian Deep Learning: A Survey
Awesome Bayesian Deep Learning375
6 years ago
A curated list of resources dedicated to bayesian deep learning
a month ago9bsd-2-clauseJupyter Notebook
PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks
5 years ago5Python
Code for the paper "Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks"
Bayesian Neural Network Pytorch241
8 months ago6mitPython
PyTorch implementation of bayesian neural network.
4 years ago
Papers for Bayesian-NN
Bayesian Neural Network Mnist212
4 years ago3Jupyter Notebook
Bayesian neural network using Pyro and PyTorch on MNIST dataset
4 years ago2mitPython
Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al
Alternatives To Bayesian Analysis Recipes
Select To Compare

Alternative Project Comparisons




I've recently been inspired by how flexible and powerful Bayesian statistical analysis can be. Yet, as with many things, flexibility often means a tradeoff with ease-of-use. I think having a cookbook of code that can be used in a number of settings can be extremely helpful for bringing Bayesian methods to a more general setting!


My goal here is to have one notebook per model. In each notebook, you should end up finding:

  • The kind of problem that is being tackled here.
  • A description of how the data should be structured.
  • An example data table. It generally will end up being tidy data.
  • PyMC3 code for the model; in some notebooks, there may be two versions of the same model.
  • Examples on how to report findings from the MCMC-sampled posterior.

It is my hope that these recipes will be useful for you!


My hypothesis here follows the Pareto principle: a large fraction of real-world problems can essentially be boiled down to a few categories of problems, which have a Bayesian interpretation.

In particular, I have this hunch that commonly-used methods like ANOVA can be replaced by conceptually simpler and much more interpretable Bayesian alternatives, like John Kruschke's BEST (Bayesian Estimation Supersedes the T-test). For example, ANOVA only tests whether means of multiple treatment groups are the same or not... but BEST gives us the estimated posterior distribution over each of the treatment groups, assuming each treatment group is identical. Hence, richer information can be gleaned: we can, given the data at hand, make statements about how any particular pair of groups are different, without requiring additional steps such as multiple hypothesis corrections etc.

further reading/watching/listening




got feedback?

There's a few ways you can help make this repository an awesome one for Bayesian method learners out there.

  1. If you have a question: Post a GitHub issue with your question. I'll try my best to respond.
  2. If you have a suggested change: Submit a pull request detailing the change and why you think it's important. Keep it simple, no need to have essay-length justifications.
Popular Bayesian Projects
Popular Neural Network Projects
Popular Machine Learning Categories
Related Searches

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
Jupyter Notebook
Neural Network
Bayesian Inference
Statistical Analysis
Bayesian Statistics
Bayesian Methods
Bayesian Data Analysis