Reconstructing probability distributions with Gaussian processes

Thomas McClintock, Eduardo Rozo

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Modern 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 amethod 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 newchainwith 40 times as many points in only 30min.Our likelihood reconstruction tool is made publicly available online.

Original languageEnglish (US)
Pages (from-to)4155-4160
Number of pages6
JournalMonthly Notices of the Royal Astronomical Society
Issue number3
StatePublished - Nov 1 2019


  • Methods: Data analysis

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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