Bayesian selection of misspecified models is overconfident and may cause spurious posterior probabilities for phylogenetic trees.

ABSTRACT: The Bayesian method is noted to produce spuriously high posterior probabilities for phylogenetic trees in analysis of large datasets, but the precise reasons for this overconfidence are unknown. In general, the performance of Bayesian selection of misspecified models is poorly understood, even though this is of great scientific interest since models are never true in real data analysis. Here we characterize the asymptotic behavior of Bayesian model selection and show that when the competing models are equally wrong, Bayesian model selection exhibits surprising and polarized behaviors in large datasets, supporting one model with full force while rejecting the others. If one model is slightly less wrong than the other, the less wrong model will eventually win when the amount of data increase

SUBMITTER: Yang Z 

PROVIDER: S-EPMC5828583 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature