  pocoMC is a Python package for accelerated Bayesian inference in astronomy
and cosmology. The code is designed to sample efficiently from posterior
distributions with non-trivial geometry, including strong multimodality and
non-linearity. To this end, pocoMC relies on the Preconditioned Monte Carlo
algorithm which utilises a Normalising Flow in order to decorrelate the
parameters of the posterior. It facilitates both tasks of parameter estimation
and model comparison, focusing especially on computationally expensive
applications. It allows fitting arbitrary models defined as a log-likelihood
function and a log-prior probability density function in Python. Compared to
popular alternatives (e.g. nested sampling) pocoMC can speed up the sampling
procedure by orders of magnitude, cutting down the computational cost
substantially. Finally, parallelisation to computing clusters manifests linear
scaling.
