Bayesian Modeling and Probabilistic Programming in Python
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
The Python ensemble sampling toolkit for affine-invariant MCMC
Collection of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) algorithms applied on simple examples.
A batteries-included toolkit for the GPU-accelerated OpenMM molecular simulation engine.
Collection of probabilistic models and inference algorithms
⚡️ zeus: Lightning Fast MCMC ⚡️
Manifold Markov chain Monte Carlo methods in Python
Fast & scalable MCMC for all your exoplanet needs!
Normalizing-flow enhanced sampling package for probabilistic inference in Jax
MCMC sample analysis, kernel densities, plotting, and GUI
Use MCMC to analyze districting plans and gerrymanders
pocoMC: A Python implementation of Preconditioned Monte Carlo for accelerated Bayesian Computation
Python library for stochastic numerical optimization
Geophysical Bayesian Inference in Python. Docs:
VIP is a python package/library for angular, reference star and spectral differential imaging for exoplanet/disk detection through high-contrast imaging.
PyAutoFit: Classy Probabilistic Programming
Nested Sampling post-processing and plotting
A lightweight and performant implementation of HMC and NUTS in Python, spun out of the PyMC project.
Add a description, image, and links to the mcmc topic page so that developers can more easily learn about it.
To associate your repository with the mcmc topic, visit your repo's landing page and select "manage topics."