期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 489, 期 3, 页码 4155-4160出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stz2426
关键词
methods: data analysis
资金
- DOE [DE-SC0015975, FG-2016-6443]
- Cottrell Scholar program of the Research Corporation for Science Advancement
- U.S. Department of Energy (DOE) [DE-SC0015975] Funding Source: U.S. Department of Energy (DOE)
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). 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 30min. Our likelihood reconstruction tool is made publicly available online.
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