Bayesian inference with probabilistic programming.
BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.
Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
Manifold Markov chain Monte Carlo methods in Python
A C++ library of Markov Chain Monte Carlo (MCMC) methods
Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python
🍓 Simulation-free, GPU-first generative modeling in PyTorch ⚡ Composable primitives for scalable, stable training of modern EBMs, diffusion, flow matching, and Schrödinger bridges.
🚫 ↩️ A document that introduces Bayesian data analysis.
By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.
Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real-world datasets.
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).
Official code for HH-VAEM
Exact Hamiltonian Monte Carlo Sampler for Truncated Multivariate Gaussians