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

2022/07/12 by Minas Karamanis, David Nabergoj, Karamanis, Minas +7 · 8 citations
Computer Science · Mathematics · #Computational Physics (physics.comp-ph) #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Statistical Distribution Estimation and Applications #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.2207.05660

openalex publication_date 2022/07/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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