FlowMC

Normalizing-flow enhanced sampling package for probabilistic inference

FlowMC is a Jax-based python package for normalizing-flow enhanced Markov chain Monte Carlo (MCMC) sampling. The code is open source under MIT license, and it is under active development.

• Just-in-time compilation is supported.
• Native support for GPU acceleration.
• Suit for problems with multi-modality.
• Minimal tuning.

Installation

Our package is still in development stage, so it has not reached the official PyPi index yet. To install our package, run the following command:

pip install flowMC

Requirements

Here is a list of packages we use in the main library

* Python 3.8+
* Jax
* Jaxlib
* Flax

To visualize the inference results in the examples, we requrie the following packages in addtion to the above:

* matplotlib
* corner
* arviz

Attribution

A Jax implementation of methods described in:

Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods Gabrié M., Rotskoff G. M., Vanden-Eijnden E. - ICML INNF+ workshop 2021 - pdf

Adaptive Monte Carlo augmented with normalizing flows. Gabrié M., Rotskoff G. M., Vanden-Eijnden E. - PNAS 2022 - doi, arxiv