Bayesian Model Selection with an Application to Cosmology

TL;DR

This paper applies Bayesian methods to compare cosmological models using supernova data, finding that the wCDM model is slightly favored over Lambda CDM and CPL models in describing cosmic expansion.

Contribution

It introduces a Bayesian framework for cosmological model selection using Hamiltonian Monte Carlo and Bayes factors, demonstrating its application to real supernova data.

Findings

wCDM model has stronger Bayesian evidence than Lambda CDM and CPL

All models show similar predictive performance

Bayesian approach effectively compares cosmological models

Abstract

We investigate cosmological parameter inference and model selection from a Bayesian perspective. Type Ia supernova data from the Dark Energy Survey (DES-SN5YR) are used to test the $\Lambda$CDM, $w$CDM, and CPL cosmological models. Posterior inference is performed via Hamiltonian Monte Carlo using the No-U-Turn Sampler (NUTS) implemented in NumPyro and analyzed with ArviZ in Python. Bayesian model comparison is conducted through Bayes factors computed using the bridgesampling library in R. The results indicate that all three models demonstrate similar predictive performance, but $w$CDM shows stronger evidence relative to $\Lambda$CDM and CPL. We conclude that, under the assumptions and data used in this study, $w$CDM provides a better description of cosmological expansion.