MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics

TL;DR

MultiNest is a robust Bayesian inference tool that efficiently computes evidence and samples from complex, multimodal distributions, significantly improving over previous algorithms in cosmology and particle physics applications.

Contribution

The paper introduces an improved, publicly available multimodal nested sampling algorithm, MultiNest, with enhanced efficiency and robustness for high-dimensional Bayesian inference.

Findings

Demonstrated accuracy on toy problems

Applied to cosmological models with extended parameters

Achieved efficient sampling of multimodal distributions

Abstract

We present further development and the first public release of our multimodal nested sampling algorithm, called MultiNest. This Bayesian inference tool calculates the evidence, with an associated error estimate, and produces posterior samples from distributions that may contain multiple modes and pronounced (curving) degeneracies in high dimensions. The developments presented here lead to further substantial improvements in sampling efficiency and robustness, as compared to the original algorithm presented in Feroz & Hobson (2008), which itself significantly outperformed existing MCMC techniques in a wide range of astrophysical inference problems. The accuracy and economy of the MultiNest algorithm is demonstrated by application to two toy problems and to a cosmological inference problem focussing on the extension of the vanilla $\Lambda$CDM model to include spatial curvature and a varying equation of state for dark energy. The MultiNest software, which is fully parallelized using MPI and includes an interface to CosmoMC, is available at http://www.mrao.cam.ac.uk/software/multinest/. It will also be released as part of the SuperBayeS package, for the analysis of supersymmetric theories of particle physics, at http://www.superbayes.org