Recent Advances in Bayesian Inference in Cosmology and Astroparticle Physics Thanks to the MultiNest Algorithm

Abstract

We present a new algorithm, called MultiNest, which is a highly efficient alternative to traditional Markov Chain Monte Carlo (MCMC) sampling of posterior distributions. MultiNest is more efficient than MCMC, can deal with highly multi-modal likelihoods and returns the Bayesian evidence (or model likelihood, the prime quantity for Bayesian model comparison) together with posterior samples. It can thus be used as an all-around Bayesian inference engine. When appropriately tuned, it also provides an exploration of the profile likelihood that is competitive with what can be obtained with dedicated algorithms.We demonstrate the power and flexibility of MultiNest for Bayesian inference for multi-dimensional, multimodal-likelihoods, for Bayesian model selection and for profile likelihood evaluation for multi-modal, multi-scale likelihoods. Applications in cosmology and astroparticle physics are presented, including gravitational waves astronomy, inflationary Bayesian model comparison and supersymmetric parameter spaces exploration.KeywordsPosterior DistributionGravitational WaveProfile LikelihoodBayesian Model SelectionNest SamplingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.