MultiNest

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 http://www.mrao.cam.ac.uk/software/multinest/. It will also be released as part of the SuperBayeS package, for the analysis of supersymmetric theories of particle physics, at http://www.superbayes.org.


References in zbMATH (referenced in 50 articles )

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  1. Martin, Sergio M.; Wälchli, Daniel; Arampatzis, Georgios; Economides, Athena E.; Karnakov, Petr; Koumoutsakos, Petros: Korali: efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization (2022)
  2. Giesel, Eileen; Reischke, Robert; Malte Schäfer, Björn; Chia, Dominic: Information geometry in cosmological inference problems (2021)
  3. Johannes Buchner: UltraNest - a robust, general purpose Bayesian inference engine (2021) not zbMATH
  4. Johannes Buchner: Bayesian X-ray Analysis (BXA) v4.0 (2021) not zbMATH
  5. Moradinezhad Dizgah, Azadeh; Biagetti, Matteo; Sefusatti, Emiliano; Desjacques, Vincent; Noreña, Jorge: Primordial non-Gaussianity from biased tracers: likelihood analysis of real-space power spectrum and bispectrum (2021)
  6. Zhang, Xinyi; Zhou, Shun: Inverse seesaw model with a modular (S_4) symmetry: lepton flavor mixing and warm dark matter (2021)
  7. Carloni, S.; Fatibene, L.; Ferraris, M.; McLenaghan, R. G.; Pinto, P.: Discrete relativistic positioning systems (2020)
  8. James R. Paynter: PyGRB: A pure Python gamma-ray burst analysis package (2020) not zbMATH
  9. Joshua G. Albert: JAXNS: a high-performance nested sampling package based on JAX (2020) arXiv
  10. Pompe, Emilia; Holmes, Chris; Łatuszyński, Krzysztof: A framework for adaptive MCMC targeting multimodal distributions (2020)
  11. 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)
  12. Cedeño, Francisco X. Linares; Montiel, Ariadna; Hidalgo, Juan Carlos; German, Gabriel: Bayesian evidence for (\alpha)-attractor dark energy models (2019)
  13. Chen, Xi; Hobson, Michael; Das, Saptarshi; Gelderblom, Paul: Improving the efficiency and robustness of nested sampling using posterior repartitioning (2019)
  14. Higson, Edward; Handley, Will; Hobson, Michael; Lasenby, Anthony: Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation (2019)
  15. Huang, Guo-yuan; Zhou, Shun: Impact of an eV-mass sterile neutrino on the neutrinoless double-beta decays: a Bayesian analysis (2019)
  16. Joshua S Speagle: dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences (2019) arXiv
  17. Nezhad, H. Bouzari; Shojai, F.: Trans-Planckian effects in warm inflation (2019)
  18. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  19. Brendon Brewer; Daniel Foreman-Mackey: DNest4: Diffusive Nested Sampling in C++ and Python (2018) not zbMATH
  20. Dinda, Bikash R.: Weak lensing probe of cubic Galileon model (2018)

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