cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation

TL;DR

cosmoabc is a flexible Python-based likelihood-free inference tool using Population Monte Carlo for complex models, demonstrated on cosmological parameter estimation from galaxy cluster counts without explicit likelihood calculations.

Contribution

it introduces cosmoabc, a new ABC sampler with adaptive importance sampling, adaptable to external simulators and arbitrary distance and prior functions.

Findings

Successfully estimates cosmological parameters from galaxy cluster counts.

Demonstrates flexibility and ease of coupling with external simulators.

Provides an open-source, well-documented Python package for likelihood-free inference.

Abstract

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present cosmoabc, a Python ABC sampler featuring a Population Monte Carlo (PMC) variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled cosmoabc with the numcosmo library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. cosmoabc is published under the GPLv3 license on PyPI and GitHub and documentation is available at http://goo.gl/SmB8EX