title: The summary-likelihood method and its implementation in the Infusion package.
abstract: In recent years, simulation methods such as approximate Bayesian computation have extensively been used to infer parameters of population genetic models where the likelihood is intractable. We describe an alternative approach, summary likelihood, that provides a likelihood-based analysis of the information retained in the summary statistics whose distribution is simulated. We provide an automated implementation as a standard R package, Infusion, and we test the method, in particular for a scenario of inference of population-size change from genetic data. We show that the method provides confidence intervals with controlled coverage independently of a prior distribution on parameters, in contrast to approximate Bayesian computation. We expect the method to be applicable for at least six-parameter models and discuss possible modifications for higher-dimensional inference problems.
authors: CNRS, IRD, EPHE, CC065, Institut des Sciences de l'Évolution, University of Montpellier, Pl. E. Bataillon, 34095, Montpellier, France.|Institut de Biologie Computationnelle, University of Montpellier, CC05019, 860 rue St Priest, 34095 Montpellier, France.
authors: François
authors: F
authors: Rousset
authors: CNRS, IRD, EPHE, CC065, Institut des Sciences de l'Évolution, University of Montpellier, Pl. E. Bataillon, 34095, Montpellier, France.|Institute of Ecology and Evolution, University of Berne, Baltzerstrasse 6, CH-3012, Berne, Switzerland.
authors: Alexandre
authors: A
authors: Gouy
authors: CNRS, IRD, EPHE, CC065, Institut des Sciences de l'Évolution, University of Montpellier, Pl. E. Bataillon, 34095, Montpellier, France.
authors: Camille
authors: C
authors: Martinez-Almoyna
authors: Leibniz Institute for Zoo and Wildlife Research, 10315, Berlin, Germany.|Berlin Center for Genomics in Biodiversity Research (BeGenDiv), 14195, Berlin, Germany.
authors: Alexandre
authors: A
authors: Courtiol
mesh_terms: D056808:Biostatistics / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D011156:Population Density; D012984:Software
keywords: approximate Bayesian computation; demographic history; likelihood inference; simulation