Abstract:
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Estimating evolutionary trees, called phylogenies or genealogies, is a fundamental task in modern biology. Once phylogenetic reconstruction is accomplished, scientists are faced with a challenging problem of interpreting phylogenetic trees. In certain situations, a coalescent process, a stochastic model that randomly generates evolutionary trees, comes to rescue by probabilistically connecting phylogenetic reconstruction with the demographic history of the population under study. An important application of the coalescent is phylodynamics, an area that aims at reconstructing past population dynamics from genomic data. Phylodynamic methods have been especially successful in analyses of genetic sequences from viruses circulating in human populations. From a Bayesian hierarchal modeling perspective, the coalescent process can be viewed as a prior for evolutionary trees, parameterized in terms of unknown demographic parameters, such as the population size trajectory. I will review Bayesian nonparametric techniques that can accomplish phylodynamic reconstruction, with a particular attention to analysis of genetic data sampled serially through time.
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