2004/12/31 by Phil Marshall, Philip J. Marshall, Nutan Rajguru +3 · 1 citation
Mathematics · Physics and Astronomy · #Artificial intelligence #Astrophysics #Bayesian probability #CMB cold spot #COSMIC cancer database #Computer science #Consistency (knowledge bases) #Cosmic microwave background #Cosmology #Cosmology and Gravitation Theories #Data set #Galaxies: Formation, Evolution, Phenomena #Galaxy #Gamma-ray bursts and supernovae #Mathematics #Physics #Redshift #Set (abstract data type) #Statistics #astro-ph
paper · pdf · doi:10.1103/physrevd.73.067302
published as Phys.Rev.D73:067302,2006 · 4 pages, accepted by Phys. Rev. D
openalex publication_date 2006/03/17 · arxiv created 2007/10/30 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We introduce a new conservative test for quantifying the consistency of two or more datasets. The test is based on the Bayesian answer to the question, ``How much more probable is it that all my data were generated from the same model system than if each dataset were generated from an independent set of model parameters?'' We make explicit the connection between evidence ratios and the differences in peak chi-squared values, the latter of which are more widely used and more cheaply calculated. Calculating evidence ratios for three cosmological datasets [recent cosmic microwave background data (WMAP, ACBAR, CBI, VSA), SDSS galaxy redshift survey, and the most recent SNe type 1A data] we find that concordance is favored and the tightening of constraints on cosmological parameters is indeed justified.