Applications of Bayesian model selection to cosmological parameters

Abstract

Bayesian model selection is a tool for deciding whether the introduction of a new parameter is warranted by the data. I argue that the usual sampling statistic significance tests for a null hypothesis can be misleading, since they do not take into account the information gained through the data, when updating the prior distribution to the posterior. In contrast, Bayesian model selection offers a quantitative implementation of Occam's razor.

Identifiers

DOI

10.1111/j.1365-2966.2007.11738.x

Citations and references