6533b82dfe1ef96bd1290b2a

RESEARCH PRODUCT

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

Roberto TrottaRoberto Ruiz De AustriMichael P. HobsonFarhan Feroz

subject

Astroparticle physicsPhysicsPosterior probabilitySampling (statistics)Markov chain Monte CarloBayesian evidenceBayesian inferenceCosmologyPrime (order theory)Statistics::Computationsymbols.namesakeSettore FIS/05 - Astronomia e AstrofisicasymbolsStatistics::MethodologyAlgorithmComputer Science::Databases

description

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.

yearjournalcountryeditionlanguage
2012-10-07
https://doi.org/10.1007/978-1-4614-3508-2_6