Bayesian evidence for the tensor-to-scalar ratio $r$ and neutrino masses $m_\nu$: Effects of uniform vs logarithmic priors

TL;DR

This paper examines how the choice of uniform versus logarithmic priors affects Bayesian evidence and model comparison for the tensor-to-scalar ratio and neutrino masses using Planck and neutrino oscillation data.

Contribution

It provides a detailed analysis of prior effects on Bayesian evidence, highlighting the invariance of evidence to lower bounds and quantifying Occam penalties with Kullback-Leibler divergence.

Findings

Uniform prior on r disfavors the r-extension model (odds 1:20).

Logarithmic prior makes the base LCDM and extended models equally likely.

Favorability of neutrino mass models varies significantly with prior choice.

Abstract

We review the effect that the choice of a uniform or logarithmic prior has on the Bayesian evidence and hence on Bayesian model comparisons when data provide only a one-sided bound on a parameter. We investigate two particular examples: the tensor-to-scalar ratio $r$ of primordial perturbations and the mass of individual neutrinos $m_\nu$, using the cosmic microwave background temperature and polarisation data from Planck 2018 and the NuFIT 5.0 data from neutrino oscillation experiments. We argue that the Kullback-Leibler divergence, also called the relative entropy, mathematically quantifies the Occam penalty. We further show how the Bayesian evidence stays invariant upon changing the lower prior bound of an upper constrained parameter. While a uniform prior on the tensor-to-scalar ratio disfavours the $r$-extension compared to the base LCDM model with odds of about 1:20, switching to a logarithmic prior renders both models essentially equally likely. LCDM with a single massive neutrino is favoured over an extension with variable neutrino masses with odds of 20:1 in case of a uniform prior on the lightest neutrino mass, which decreases to roughly 2:1 for a logarithmic prior. For both prior options we get only a very slight preference for the normal over the inverted neutrino hierarchy with Bayesian odds of about 3:2 at most.