Cosmic Bayes: Datasets and priors in the hunt for dark energy

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Bayesian methods are ubiquitous in contemporary observational cosmology. They enter into three main tasks: (I) cross-checking datasets for consistency; (II) fixing constraints on cosmological parameters; and (III) model selection. This article explores some epistemic limits of using Bayesian methods. The first limit concerns the degree of informativeness of the Bayesian priors and an ensuing methodological tension between task (I) and task (II). The second limit concerns the choice of wide flat priors and related tension between (II) parameter estimation and (III) model selection. The Dark Energy Survey (DES) and its recent Year 1 results illustrate both these limits concerning the use of Bayesianism.
Original languageEnglish
Article number29
Pages (from-to)1-21
Number of pages21
JournalEuropean Journal for Philsophy of Science
Issue number1
Early online date16 Jan 2021
Publication statusPublished - 31 Mar 2021

Keywords / Materials (for Non-textual outputs)

  • philosophy of cosmology
  • dark energy
  • datasets consistency
  • dark energy survey
  • priors
  • Bayes factor
  • Jeffreys scale


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