A parametric interpretation of Bayesian Nonparametric Inference from Gene Genealogies: Linking ecological, population genetics and evolutionary processes.

摘要: Abstract Using a nonparametric Bayesian approach Palacios and Minin (2013) dramatically improved the accuracy, precision of inference population size trajectories from gene genealogies. These authors proposed an extension Gaussian Process (GP) inferential method for intensity function non-homogeneous Poisson processes. They found that not only statistical properties estimators were with their method, but also, key aspects demographic histories recovered. The authors’ work represents first solution to this problem because they specify convenient prior belief without particular functional form on trajectory. Their works so well provides such profound understanding biological process, question arises as how truly “biology-free” really is. well-known concepts stochastic dynamics, here I demonstrate in fact, Minin’s GP model can be cast parametric growth density dependence environmental stochasticity. Making link between genetics dynamics modeling novel insights into eliciting biologically meaningful priors trajectory effective size. results presented also bring models evolution trait. Thus, ecological principles foundation (2013)’s adds conceptual scientific value these approach. conclude note by listing series brought about connection Ecology.