title: Postprocessing of genealogical trees.
abstract: We consider inference for demographic models and parameters based upon postprocessing the output of an MCMC method that generates samples of genealogical trees (from the posterior distribution for a specific prior distribution of the genealogy). This approach has the advantage of taking account of the uncertainty in the inference for the tree when making inferences about the demographic model and can be computationally efficient in terms of reanalyzing data under a wide variety of models. We consider a (simulation-consistent) estimate of the likelihood for variable population size models, which uses importance sampling, and propose two new approximate likelihoods, one for migration models and one for continuous spatial models.
authors.affiliation: Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, United Kingdom. l.meligotsidou@lancaster.ac.uk
authors.forename: Loukia
authors.initials: L
authors.lastname: Meligkotsidou
authors.forename: Paul
authors.initials: P
authors.lastname: Fearnhead
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D004247:DNA / Q000235:genetics; D003627:Data Interpretation, Statistical; D004641:Emigration and Immigration; D019143:Evolution, Molecular; D005796:Genes / Q000502:physiology*; D014644:Genetic Variation; D005828:Genetics, Population; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D010375:Pedigree; D012984:Software
chemical_list: D004247:DNA