Identifiability of parameters in MCMC Bayesian inference of phylogeny.
Methods for Bayesian inference of phylogeny using DNA sequences based on Markov chain Monte Carlo (MCMC) techniques allow the incorporation of arbitrarily complex models of the DNA substitution process, and other aspects of evolution. This has increased the realism of models, potentially improving the accuracy of the methods, and is largely responsible for their recent popularity. Another consequence of the increased complexity of models in Bayesian phylogenetics is that these models have, in several cases, become overparameterized. In such cases, some parameters of the model are not identifiable; different combinations of nonidentifiable parameters lead to the same likelihood, making it impossible to decide among the potential parameter values based on the data. Overparameterized models can also slow the rate of convergence of MCMC algorithms due to large negative correlations among parameters in the posterior probability distribution. Functions of parameters can sometimes be found, in overparameterized models, that are identifiable, and inferences based on these functions are legitimate. Examples are presented of overparameterized models that have been proposed in the context of several Bayesian methods for inferring the relative ages of nodes in a phylogeny when the substitution rate evolves over time.
Rannala
Bruce
B
Department of Medical Genetics, University of Alberta, Edmonton, Alberta, T6G 2H7, Canada.
D000465:Algorithms; D001499:Bayes Theorem; D005075:Biological Evolution; D019143:Evolution, Molecular; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D010802:Phylogeny; D013997:Time Factors