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We present further development and the first public release of our multimodalnested sampling algorithm, called MultiNest. This Bayesian inference toolcalculates the evidence, with an associated error estimate, and producesposterior samples from distributions that may contain multiple modes andpronounced (curving) degeneracies in high dimensions. The developmentspresented here lead to further substantial improvements in sampling efficiencyand robustness, as compared to the original algorithm presented in Feroz &Hobson (2008), which itself significantly outperformed existing MCMC techniquesin a wide range of astrophysical inference problems. The accuracy and economyof the MultiNest algorithm is demonstrated by application to two toy problemsand to a cosmological inference problem focussing on the extension of thevanilla $\Lambda$CDM model to include spatial curvature and a varying equationof state for dark energy. The MultiNest software, which is fully parallelizedusing MPI and includes an interface to CosmoMC, is available athttp://www.mrao.cam.ac.uk/software/multinest/. It will also be released as partof the SuperBayeS package, for the analysis of supersymmetric theories ofparticle physics, at http://www.superbayes.org

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