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pocoMC is a Python package for accelerated Bayesian inference in astronomyand cosmology. The code is designed to sample efficiently from posteriordistributions with non-trivial geometry, including strong multimodality andnon-linearity. To this end, pocoMC relies on the Preconditioned Monte Carloalgorithm which utilises a Normalising Flow in order to decorrelate theparameters of the posterior. It facilitates both tasks of parameter estimationand model comparison, focusing especially on computationally expensiveapplications. It allows fitting arbitrary models defined as a log-likelihoodfunction and a log-prior probability density function in Python. Compared topopular alternatives (e.g. nested sampling) pocoMC can speed up the samplingprocedure by orders of magnitude, cutting down the computational costsubstantially. Finally, parallelisation to computing clusters manifests linearscaling.

pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology