Accelerating astronomical and cosmological inference with preconditioned Monte Carlo

Karamanis, Minas (author), Beutler, Florian (author), Peacock, John A. (author), Nabergoj, David (author), Seljak, Uroš (author)

We introduce preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference that facilitates efficient sampling of probability distributions with non-trivial geometry. PMC utilizes a Normalizing Flow (NF) in order to decorrelate the parameters of the distribution and then proceeds by sampling from the preconditioned target distribution using an adaptive Sequential Monte Carlo (SMC) scheme. The results produced by PMC include samples from the posterior distribution and an estimate of the model evidence that can be used for parameter inference and model comparison, respectively. The aforementioned framework has been thoroughly tested in a variety of challenging target distributions achieving state-of-the-art sampling performance. In the cases of primordial feature analysis and gravitational wave inference, PMC is approximately 50 and 25 times faster, respectively, than nested sampling (NS). We found that in higher dimensional applications, the acceleration is even greater. Finally, PMC is directly parallelisable, manifesting linear scaling up to thousands of CPUs.

• Language: English
• Keywords: data analysis, statistics, large-scale structure of the Universe
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Publication date: 01.10.2022
• Year: 2022
• Number of pages: Str. 1644-1653
• Numbering: Vol. 516, iss. 2
• PID: 20.500.12556/RUL-171138
• UDC: 524.8:536.911
• ISSN on article: 0035-8711
• DOI: 10.1093/mnras/stac2272
• COBISS.SI-ID: 243782403
• Publication date in RUL: 08.08.2025

Record is a part of a journal

• Title: Monthly notices of the royal astronomical society
• Shortened title: Mon. Not. R. Astron. Soc.
• Publisher: Blackwell Scientific Publications
• ISSN: 0035-8711
• COBISS.SI-ID: 25980416

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: analiza podatkov, statistika, struktura Vesolja

Projects

• Funder: Other - Other funder or multiple funders
• Project number: 853291

• Funder: Other - Other funder or multiple funders
• Project number: DE-AC02-05CH11231