BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.

Manifold Markov chain Monte Carlo methods in Python

PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.

Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods

The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural Networks (DNNs), Neural ODEs, and Symplectic Neural Networks (SympNets) used with state-of-the-art sampling schemes like Hamiltonian Monte Carlo (HMC) and the No-U-Turn-Sampler (NUTS).

PinNUTS🥜 is dynamic Hamiltonian Monte Carlo algorithm implemented in Python

Official code for HH-VAEM

An experimental Python package for learning Bayesian Neural Network.

Hamiltonian Annealed Importance Sampling (HAIS) in tensorflow

Monte is a set of Monte Carlo methods in Python. The package is written to be flexible, clear to understand and encompass variety of Monte Carlo methods.

Code implementing Integrator Snippets, joint work with Christophe Andrieu and Chang Zhang

A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)

Statistics and Machine Learning in depth analysis with Tensorflow Probability

Bayesian Inference of open cluster ages from photometry, parallaxes and Lithium measurements.

This project contains the implementation of using the generative models in continuous authentication on motion sensor data from the H-MOG dataset.

This repo implements Robert, Wu, Stoehr, CP Robert - 2019 (https://arxiv.org/abs/1810.04449) algorithms eHMC and prHMC

Theano implementations of thermodynamic Monte Carlo algorithms

Using the Hamiltonian Monte Carlo algorithm, and a visualisation of how the algorithm works. 'MyMC3' - HMC is the main algorithm used in Bayesian statistical package PyMC3.

Implementation of folded Folded Hamiltonian Monte Carlo for data imputation and augmentation on data collected from cancer patients who self-reported their symptoms experience during chemotherapy by a team in the School of the Nursing University of California.