Bayesian inference with probabilistic programming.

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

Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

Manifold Markov chain Monte Carlo methods in Python

A C++ library of Markov Chain Monte Carlo (MCMC) methods

🚫 ↩️ A document that introduces Bayesian data analysis.

Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python

By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

Application of the L2HMC algorithm to simulations in lattice QCD.

David Mackay's book review and problem solvings and own python codes, mathematica files

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

Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.

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).

Exact Hamiltonian Monte Carlo Sampler for Binary Distributions

PinNUTS🥜 is dynamic Hamiltonian Monte Carlo algorithm implemented in Python

Exact Hamiltonian Monte Carlo Sampler for Truncated Multivariate Gaussians

Official code for HH-VAEM