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 26 articles )

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  1. Higson, Edward; Handley, Will; Hobson, Michael; Lasenby, Anthony: Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation (2019)
  2. Joshua S Speagle: dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences (2019) arXiv
  3. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  4. Brendon Brewer; Daniel Foreman-Mackey: DNest4: Diffusive Nested Sampling in C++ and Python (2018) not zbMATH
  5. Edward Higson: dyPolyChord: dynamic nested sampling with PolyChord (2018) not zbMATH
  6. Edward Higson, Will Handley, Mike Hobson, Anthony Lasenby: nestcheck: diagnostic tests for nested sampling calculations (2018) arXiv
  7. Higson, Edward; Handley, Will; Hobson, Mike; Lasenby, Anthony: Sampling errors in nested sampling parameter estimation (2018)
  8. Ohlsson, Tommy; Pernow, Marcus: Running of fermion observables in non-supersymmetric SO(10) models (2018)
  9. Akula, Sujeet; Balázs, Csaba; Dunn, Liam; White, Graham: Electroweak baryogenesis in the ( \mathbbZ_3 )-invariant NMSSM (2017)
  10. 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)
  11. Hrycyna, Orest: What (\xi)? Cosmological constraints on the non-minimal coupling constant (2017)
  12. Jubb, Thomas; Kirk, Matthew; Lenz, Alexander: Charming dark matter (2017)
  13. Meloni, Davide; Ohlsson, Tommy; Riad, Stella: Renormalization group running of fermion observables in an extended non-supersymmetric SO(10) model (2017)
  14. Buchner, Johannes: A statistical test for nested sampling algorithms (2016)
  15. Enberg, Rikard; Klemm, William; Moretti, Stefano; Munir, Shoaib; Wouda, Glenn: Charged Higgs boson in the (W^\pm) Higgs channel at the Large Hadron Collider (2015)
  16. Madireddy, Sandeep; Sista, Bhargava; Vemaganti, Kumar: A Bayesian approach to selecting hyperelastic constitutive models of soft tissue (2015)
  17. Perrakis, Konstantinos; Ntzoufras, Ioannis; Tsionas, Efthymios G.: On the use of marginal posteriors in marginal likelihood estimation via importance sampling (2014)
  18. Yee, Eugene: Inverse dispersion for an unknown number of sources: model selection and uncertainty analysis (2012)
  19. Brewer, Brendon J.; Pártay, Livia B.; Csányi, Gábor: Diffusive nested sampling (2011)
  20. 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)

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