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 39 articles )

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  1. Johannes Buchner: Bayesian X-ray Analysis (BXA) v4.0 (2021) not zbMATH
  2. Johannes Buchner: UltraNest - a robust, general purpose Bayesian inference engine (2021) not zbMATH
  3. Carloni, S.; Fatibene, L.; Ferraris, M.; McLenaghan, R. G.; Pinto, P.: Discrete relativistic positioning systems (2020)
  4. James R. Paynter: PyGRB: A pure Python gamma-ray burst analysis package (2020) not zbMATH
  5. Joshua G. Albert: JAXNS: a high-performance nested sampling package based on JAX (2020) arXiv
  6. Pompe, Emilia; Holmes, Chris; Łatuszyński, Krzysztof: A framework for adaptive MCMC targeting multimodal distributions (2020)
  7. Asaadi, Erfan; Heyns, P. Stephan; Haftka, Raphael T.; Tootkaboni, Mazdak: On the value of test data for reducing uncertainty in material models: computational framework and application to spherical indentation (2019)
  8. Chen, Xi; Hobson, Michael; Das, Saptarshi; Gelderblom, Paul: Improving the efficiency and robustness of nested sampling using posterior repartitioning (2019)
  9. Higson, Edward; Handley, Will; Hobson, Michael; Lasenby, Anthony: Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation (2019)
  10. Huang, Guo-yuan; Zhou, Shun: Impact of an eV-mass sterile neutrino on the neutrinoless double-beta decays: a Bayesian analysis (2019)
  11. Joshua S Speagle: dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences (2019) arXiv
  12. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  13. Brendon Brewer; Daniel Foreman-Mackey: DNest4: Diffusive Nested Sampling in C++ and Python (2018) not zbMATH
  14. Edward Higson: dyPolyChord: dynamic nested sampling with PolyChord (2018) not zbMATH
  15. Edward Higson, Will Handley, Mike Hobson, Anthony Lasenby: nestcheck: diagnostic tests for nested sampling calculations (2018) arXiv
  16. Higson, Edward; Handley, Will; Hobson, Mike; Lasenby, Anthony: Sampling errors in nested sampling parameter estimation (2018)
  17. Ohlsson, Tommy; Pernow, Marcus: Running of fermion observables in non-supersymmetric SO(10) models (2018)
  18. Akula, Sujeet; Balázs, Csaba; Dunn, Liam; White, Graham: Electroweak baryogenesis in the ( \mathbbZ_3 )-invariant NMSSM (2017)
  19. Asaadi, Erfan; Heyns, P. Stephan: A computational framework for Bayesian inference in plasticity models characterisation (2017)
  20. Di Chiara, Stefano; Fowlie, Andrew; Fraser, Sean; Marzo, Carlo; Marzola, Luca; Raidal, Martti; Spethmann, Christian: Minimal flavor-changing (Z^\prime) models and muon (g-2) after the (R_K^\ast) measurement (2017)

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