title: Resampling: An improvement of importance sampling in varying population size models.
abstract: Sequential importance sampling algorithms have been defined to estimate likelihoods in models of ancestral population processes. However, these algorithms are based on features of the models with constant population size, and become inefficient when the population size varies in time, making likelihood-based inferences difficult in many demographic situations. In this work, we modify a previous sequential importance sampling algorithm to improve the efficiency of the likelihood estimation. Our procedure is still based on features of the model with constant size, but uses a resampling technique with a new resampling probability distribution depending on the pairwise composite likelihood. We tested our algorithm, called sequential importance sampling with resampling (SISR) on simulated data sets under different demographic cases. In most cases, we divided the computational cost by two for the same accuracy of inference, in some cases even by one hundred. This study provides the first assessment of the impact of such resampling techniques on parameter inference using sequential importance sampling, and extends the range of situations where likelihood inferences can be easily performed.
authors.affiliation: Institut Montpelliérain Alexander Grothendieck UMR CNRS 5149, Université de Montpellier, Place Eugène Bataillon, 34095 Montpellier, France; Centre de Biologie pour la Gestion des Populations, Inra, CBGP (UMR INRA / IRD / Cirad / Montpellier SupAgro), Campus International de Baillarguet, 34988 Montferrier sur Lez, France; Institut de Biologie Computationnelle, Université de Montpellier, 95 rue de la Galera, 34095 Montpellier, France. Electronic address: coralie.merle@umontpellier.fr.
authors.forename: C
authors.initials: C
authors.lastname: Merle
authors.affiliation: Centre de Biologie pour la Gestion des Populations, Inra, CBGP (UMR INRA / IRD / Cirad / Montpellier SupAgro), Campus International de Baillarguet, 34988 Montferrier sur Lez, France; Institut de Biologie Computationnelle, Université de Montpellier, 95 rue de la Galera, 34095 Montpellier, France.
authors.forename: R
authors.initials: R
authors.lastname: Leblois
authors.affiliation: Institut de Biologie Computationnelle, Université de Montpellier, 95 rue de la Galera, 34095 Montpellier, France; Institut des Sciences de l'Evolution, Université de Montpellier, CNRS, IRD, EPHE, Place Eugène Bataillon, 34392 Montpellier, France.
authors.forename: F
authors.initials: F
authors.lastname: Rousset
authors.affiliation: Institut Montpelliérain Alexander Grothendieck UMR CNRS 5149, Université de Montpellier, Place Eugène Bataillon, 34095 Montpellier, France; Institut de Biologie Computationnelle, Université de Montpellier, 95 rue de la Galera, 34095 Montpellier, France; Institut de Mathématiques de Marseille, I2M UMR 7373, Aix-Marseille Université, CNRS, Centrale Marseille, 39, rue F. Joliot Curie, 13453 Marseille, France. Electronic address: pierre.pudlo@univ-amu.fr.
authors.forename: P
authors.initials: P
authors.lastname: Pudlo
mesh_terms: D000465:Algorithms; D003710:Demography; D005828:Genetics, Population; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D011156:Population Density
keywords: Coalescent; Demographic inference; Importance sampling; Jump Markov process; Population genetics; Resampling