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Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
Bayesian inference with probabilistic programming.
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks
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 native Julia code for lattice QCD with dynamical fermions in 4 dimension.
Bayesian Generalized Linear models using `@formula` syntax.
Application of the L2HMC algorithm to simulations in lattice QCD.
A lightweight and performant implementation of HMC and NUTS in Python, spun out of the PyMC project.
A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"
AeMCMC is a Python library that automates the construction of samplers for Aesara graphs representing statistical models.
tmLQCD is a freely available software suite providing a set of tools to be used in lattice QCD simulations. This is mainly a HMC implementation (including PHMC and RHMC) for Wilson, Wilson Clover and Wilson twisted mass fermions and inverter for different versions of the Dirac operator. The code is fully parallelised and ships with optimisations for various modern architectures, such as commodity PC clusters and the Blue Gene family.
Phylogenetic inference using Stan
