Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics

Ben Moews, Joe Zuntz

Research output: Contribution to journalArticlepeer-review

Abstract

We present and apply Gaussbock, a new embarrassingly parallel iterative algorithm for cosmological parameter estimation designed for an era of cheap parallel computing resources. Gaussbock uses Bayesian nonparametrics and truncated importance sampling to accurately draw samples from posterior distributions with an orders-of-magnitude speed-up in wall time over alternative methods. Contemporary problems in this area often suffer from both increased computational costs due to high-dimensional parameter spaces and consequent excessive time requirements, as well as the need for fine tuning of proposal distributions or sampling parameters. Gaussbock is designed specifically with these issues in mind. We explore and validate the performance and convergence of the algorithm on a fast approximation to the Dark Energy Survey Year 1 (DES Y1) posterior, finding reasonable scaling behavior with the number of parameters. We then test on the full DES Y1 posterior using large-scale supercomputing facilities, and recover reasonable agreement with previous chains, although the algorithm can underestimate the tails of poorly-constrained parameters. Additionally, we discuss and demonstrate how Gaussbock recovers complex posterior shapes very well at lower dimensions, but faces challenges to perform well on such distributions in higher dimensions. In addition, we provide the community with a user-friendly software tool for accelerated cosmological parameter estimation based on the methodology described in this paper.
Original languageEnglish
Number of pages19
JournalAstrophysical Journal
Volume896
Issue number2
DOIs
Publication statusPublished - 17 Jun 2020

Keywords

  • astro-ph.CO
  • astro-ph.IM
  • stat.CO
  • stat.ME
  • 85A40, 68W10, 62G07, 62P35

Fingerprint

Dive into the research topics of 'Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics'. Together they form a unique fingerprint.

Cite this