Bayesian Model Averaging of Parametric Coalescent Models for Phylodynamic Inference.

ABSTRACT: Bayesian phylodynamic models have become essential for reconstructing population history from genetic data, yet their accuracy depends crucially on choosing appropriate demographic models. To address uncertainty in model choice, we introduce a Bayesian model averaging (BMA) framework that integrates multiple parametric coalescent models-including constant, exponential, logistic, and Gompertz growth-along with their "expansion" variants that account for non-zero ancestral populations. Implemented in a Bayesian setting with Metropolis-coupled Markov chain Monte Carlo, this approach allows the sampler to switch among candidate growth functions, thereby capturing demographic histories without having to pre-specify a single model. Simulation studies verify that the logistic and Gompertz models

SUBMITTER: Xu Y 

PROVIDER: S-EPMC12704328 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature