  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. The flowMC sampler can handle non-trivial geometry,
such as multimodal distributions and distributions with local correlations.
  The key features of flowMC are summarized in the following list: * Since
flowMC is built on top of JAX, it supports gradient-based samplers through
automatic differentiation such as MALA and Hamiltonian Monte Carlo (HMC). *
flowMC uses state-of-the-art normalizing flow models such as Rational-Quadratic
Splines to power its global sampler. These models are very efficient in
capturing important features within a relatively short training time. * Use of
accelerators such as GPUs and TPUs are natively supported. The code also
supports the use of multiple accelerators with SIMD parallelism. * By default,
Just-in-time (JIT) compilations are used to further speed up the sampling
process. * We provide a simple black box interface for the users who want to
use flowMC by its default parameters, yet provide at the same time an extensive
guide explaining trade-offs while tuning the sampler parameters. The tight
integration of all the above features makes flowMC a highly performant yet
simple- to-use package for statistical inference.
