|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Nips2017||913||5 years ago|
|A list of resources for all invited talks, tutorials, workshops and presentations at NIPS 2017|
|Bayesian Analysis Recipes||510||a year ago||2||mit||Jupyter Notebook|
|A collection of Bayesian data analysis recipes using PyMC3|
|Bayesiandeeplearning Survey||436||2 months ago|
|Bayesian Deep Learning: A Survey|
|Awesome Bayesian Deep Learning||375||6 years ago|
|A curated list of resources dedicated to bayesian deep learning|
|Hamiltorch||314||a month ago||9||bsd-2-clause||Jupyter Notebook|
|PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks|
|Vime||299||5 years ago||5||Python|
|Code for the paper "Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks"|
|Bayesian Neural Network Pytorch||241||8 months ago||6||mit||Python|
|PyTorch implementation of bayesian neural network.|
|Bayesian_neural_network_papers||218||4 years ago|
|Papers for Bayesian-NN|
|Bayesian Neural Network Mnist||212||4 years ago||3||Jupyter Notebook|
|Bayesian neural network using Pyro and PyTorch on MNIST dataset|
|Bayesianrecurrentnn||196||4 years ago||2||mit||Python|
|Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al|
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:
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.
There's a few ways you can help make this repository an awesome one for Bayesian method learners out there.