Performance of Akaike Information Criterion and Bayesian Information Criterion in Selecting Partition Models and Mixture Models.

ABSTRACT: In molecular phylogenetics, partition models and mixture models provide different approaches to accommodating heterogeneity in genomic sequencing data. Both types of models generally give a superior fit to data than models that assume the process of sequence evolution is homogeneous across sites and lineages. The Akaike Information Criterion (AIC), an estimator of Kullback-Leibler divergence, and the Bayesian Information Criterion (BIC) are popular tools to select models in phylogenetics. Recent work suggests that AIC should not be used for comparing mixture and partition models. In this work, we clarify that this difficulty is not fully explained by AIC misestimating the Kullback-Leibler divergence. We also investigate the performance of the AIC and BIC at comparing amongst mixture models

SUBMITTER: Liu Q 

PROVIDER: S-EPMC10198649 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature