Top 160 bayesian-inference open source projects

This contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.

Deep universal probabilistic programming with Python and PyTorch

PyMC3 educational resources

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

A resource list for causality in statistics, data science and physics

A high-level Bayesian analysis API written in Python

Python package for computing the Curie depth from the magnetic anomaly

Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

ggplot2 extension for plotting distributions

A python module to detect stellar flares using Bayesian model comparison

brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

Bayesian Data Analysis demos for Python

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

Resources to learn more about Machine Learning and Artificial Intelligence

RStan, the R interface to Stan

solution of exercises of the book "probabilistic robotics"

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)

Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code

How to do Bayesian statistical modelling using numpy and PyMC3

A collection of Bayesian data analysis recipes using PyMC3

Bayesian Data Analysis demos for R

Variational autoencoders for collaborative filtering

The bookdown version lives here: https://bookdown.org/content/3890

High-performance Bayesian Data Analysis on the GPU in Clojure

Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

rstanarm R package for Bayesian applied regression modeling

nway -- Bayesian cross-matching of astronomical catalogues

The Microbiome Interpretable Temporal Rule Engine

nessai: Nested Sampling with Artificial Intelligence

Material for AMLD 2020 workshop "Bayesian Inference: embracing uncertainty"

Efficient, lightweight variational inference and approximation bounds

Sleek implementations of the ZigZag, Boomerang and other assorted piecewise deterministic Markov processes for Markov Chain Monte Carlo including Sticky PDMPs for variable selection

Full Bayesian Inference for Hidden Markov Models

Particle filtering and sequential parameter inference in Python

Forecasting 🇫🇷 elections with Bayesian statistics 🥳

Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)

Helper functions for brmsfit objects (DEPRECATED)

Benchmarks of Bayesian Nonparametric models in Turing and other PPLs

Estimating effective elastic thickness of the lithosphere

Noisy network measurement with stan

Sequential Monte Carlo algorithm for approximation of posterior distributions.

A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.

Probabilistic question-asking system: the program asks, the users answer. The minimal goal of the program is to identify what the user needs (a target), even if the user is not aware of the existence of such a thing/product/service.

Machine Learning - accelerated Bayesian inference

Generation of Synthetic Populations Library

Natural Gradient, Variational Inference

R-package for interpretable nonparametric modeling of longitudinal data using additive Gaussian processes. Contains functionality for inferring covariate effects and assessing covariate relevances. Various models can be specified using a convenient formula syntax.

Data-driven Network-based Bayesian Inference of Drivers

Infer the gender of an individual based on their name.

Implementation of normalising flows and constrained random variable transformations

🎓 Uni-Bonn Decision Analysis graduate course, lectures and materials

Probabilistic Programming with Gaussian processes in Julia

Deodorant: Solving the problems of Bayesian Optimization

A collection of Methods and Models for various architectures of Artificial Neural Networks

Material for a workshop on Bayesian stats with R

Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead

Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.

Implementations of single and multi-ellipsoid nested sampling