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

An efficient Python implementation for Bayesian inference in binary stars based on Stan.

Package to do Bayesian inference with Gibbs sampler

JAX-powered Hi-Fi mocks

Bayesian Conditional Transformation Models by Manuel Carlan, Thomas Kneib and Nadja Klein

Bayesian inference using the No-U-Turn sampler.

Java and Processing implementations for visualising various MCMC methods.