flowMC: Normalizing-flow enhanced sampling package for probabilistic inference in Jax

Understand

flowMC is a Python library for accelerated Markov Chain Monte Carlo (MCMC) leveraging deep generative modeling.

• It is built on top of the machine learning libraries JAX and Flax.
• At its core, flowMC uses a local sampler and a learnable global sampler in tandem to efficiently sample posterior distributions.
• While multiple chains of the local sampler generate samples over the region of interest in the target parameter space, the package uses these samples to train a normalizing flow model, then uses it to propose global jumps across the parameter space.