Reconstructing probability distributions with Gaussian processes
Abstract
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 a method for reconstructing the log-probability distributions of completed experiments from an existing chain (or any set of posterior samples). Here, 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.
- Authors:
-
[1];
- Brookhaven National Lab. (BNL), Upton, NY (United States). Physics Dept.
- Univ. of Arizona, Tuscon, AZ (United States). Dept. of Physics
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- OSTI Identifier:
- 1573465
- Report Number(s):
- BNL-212314-2019-JAAM
Journal ID: ISSN 0035-8711; TRN: US2001369
- Grant/Contract Number:
- SC0012704
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Additional Journal Information:
- Journal Volume: 489; Journal Issue: 3; Journal ID: ISSN 0035-8711
- Publisher:
- Royal Astronomical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; methods; data analysis; statistical
Citation Formats
McClintock, Thomas, and Rozo, Eduardo. Reconstructing probability distributions with Gaussian processes. United States: N. p., 2019.
Web. doi:10.1093/mnras/stz2426.
McClintock, Thomas, & Rozo, Eduardo. Reconstructing probability distributions with Gaussian processes. United States. doi:10.1093/mnras/stz2426.
McClintock, Thomas, and Rozo, Eduardo. Mon .
"Reconstructing probability distributions with Gaussian processes". United States. doi:10.1093/mnras/stz2426. https://www.osti.gov/servlets/purl/1573465.
@article{osti_1573465,
title = {Reconstructing probability distributions with Gaussian processes},
author = {McClintock, Thomas and Rozo, Eduardo},
abstractNote = {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 a method for reconstructing the log-probability distributions of completed experiments from an existing chain (or any set of posterior samples). Here, 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.},
doi = {10.1093/mnras/stz2426},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 3,
volume = 489,
place = {United States},
year = {2019},
month = {9}
}
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