AffiliationUniv Arizona, Dept Phys
Keywordsmethods: data analysis
MetadataShow full item record
PublisherOXFORD UNIV PRESS
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/stz2426
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.
Collection InformationThis 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 email@example.com.
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.
NotePublic domain article
VersionFinal published version
SponsorsUnited States Department of Energy (DOE) [DE-SC0015975, FG-2016-6443]; Cottrell Scholar program of the Research Corporation for Science Advancement