MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics. 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, which itself significantly outperformed existing Markov chain Monte Carlo techniques in a wide range of astrophysical inference problems. The accuracy and economy of the MultiNest algorithm are demonstrated by application to two toy problems and to a cosmological inference problem focusing on the extension of the vanilla Λ cold dark matter 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 It will also be released as part of the SuperBayeS package, for the analysis of supersymmetric theories of particle physics, at

References in zbMATH (referenced in 10 articles )

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  1. Buchner, Johannes: A statistical test for nested sampling algorithms (2016)
  2. Yee, Eugene: Inverse dispersion for an unknown number of sources: model selection and uncertainty analysis (2012)
  3. Brewer, Brendon J.; Pártay, Livia B.; Csányi, Gábor: Diffusive nested sampling (2011)
  4. Bridges, Michael; Cranmer, Kyle; Feroz, Farhan; Hobson, Mike; De Austri, Roberto Ruiz; Trotta, Roberto: A coverage study of the CMSSM based on ATLAS sensitivity using fast neural networks techniques (2011)
  5. Feroz, Farhan; Cranmer, Kyle; Hobson, Mike; De Austri, Roberto Ruiz; Trotta, Roberto: Challenges of profile likelihood evaluation in multi-dimensional SUSY scans (2011)
  6. Akrami, Yashar; Scott, Pat; Edsjö, Joakim; Conrad, Jan; Bergström, Lars: A profile likelihood analysis of the constrained MSSM with genetic algorithms (2010)
  7. Cohen, Michael I.; Cutler, Curt; Vallisneri, Michele: Searches for cosmic-string gravitational-wave bursts in Mock LISA Data (2010)
  8. Feroz, Farhan; Gair, Jonathan R.; Graff, Philip; Hobson, Michael P.; Lasenby, Anthony: Classifying LISA gravitational wave burst signals using Bayesian evidence (2010)
  9. Gair, Jonathan R.; Porter, Edward K.: Cosmic swarms: a search for supermassive black holes in the LISA data stream with a hybrid evolutionary algorithm (2009)
  10. Xu, Li Xin; Liu, Hong Ya; Zhang, Cheng Wu: Reconstruction of 5D cosmological models from the equation of state of dark energy (2006)