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dc.contributor.authorMcClintock, Thomas
dc.contributor.authorRozo, Eduardo
dc.date.accessioned2019-10-31T19:59:36Z
dc.date.available2019-10-31T19:59:36Z
dc.date.issued2019-09-02
dc.identifier.citationThomas McClintock, Eduardo Rozo, Reconstructing probability distributions with Gaussian processes, Monthly Notices of the Royal Astronomical Society, Volume 489, Issue 3, November 2019, Pages 4155–4160, https://doi.org/10.1093/mnras/stz2426en_US
dc.identifier.issn0035-8711
dc.identifier.doi10.1093/mnras/stz2426
dc.identifier.urihttp://hdl.handle.net/10150/634941
dc.description.abstractModern cosmological analyses constrain physical parameters using Markov Chain Monte Carlo (MCMC) or similar sampling techniques. Oftentimes, these techniques are computationally expensive to run and require up to thousands of CPU hours to complete. Here we present a method for reconstructing the log-probability distributions of completed experiments from an existing chain (or any set of posterior samples). The reconstruction is performed using Gaussian process regression for interpolating the log-probability. This allows for easy resampling, importance sampling, marginalization, testing different samplers, investigating chain convergence, and other operations. As an example use case, we reconstruct the posterior distribution of the most recent Planck 2018 analysis. We then resample the posterior, and generate a new chain with 40 times as many points in only 30 min. Our likelihood reconstruction tool is made publicly available online.en_US
dc.description.sponsorshipUnited States Department of Energy (DOE) [DE-SC0015975, FG-2016-6443]; Cottrell Scholar program of the Research Corporation for Science Advancementen_US
dc.language.isoenen_US
dc.publisherOXFORD UNIV PRESSen_US
dc.rightsPublished by Oxford University Press on behalf of The Royal Astronomical Society 2019. This work is written by (a) US Government employee(s) and is in the public domain in the US.en_US
dc.subjectmethods: data analysisen_US
dc.titleReconstructing probability distributions with Gaussian processesen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Physen_US
dc.identifier.journalMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETYen_US
dc.description.notePublic domain articleen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.volume489
dc.source.issue3
dc.source.beginpage4155-4160
refterms.dateFOA2019-10-31T19:59:37Z


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