Bayesian Model Selection with an Application to Cosmology

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.