Likelihood-Free Inference in High-Dimensional Models.

Détails

ID Serval
serval:BIB_10589B0A38E9
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Likelihood-Free Inference in High-Dimensional Models.
Périodique
Genetics
Auteur(s)
Kousathanas A., Leuenberger C., Helfer J., Quinodoz M., Foll M., Wegmann D.
ISSN
1943-2631 (Electronic)
ISSN-L
0016-6731
Statut éditorial
Publié
Date de publication
06/2016
Peer-reviewed
Oui
Volume
203
Numéro
2
Pages
893-904
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
Methods that bypass analytical evaluations of the likelihood function have become an indispensable tool for statistical inference in many fields of science. These so-called likelihood-free methods rely on accepting and rejecting simulations based on summary statistics, which limits them to low-dimensional models for which the value of the likelihood is large enough to result in manageable acceptance rates. To get around these issues, we introduce a novel, likelihood-free Markov chain Monte Carlo (MCMC) method combining two key innovations: updating only one parameter per iteration and accepting or rejecting this update based on subsets of statistics approximately sufficient for this parameter. This increases acceptance rates dramatically, rendering this approach suitable even for models of very high dimensionality. We further derive that for linear models, a one-dimensional combination of statistics per parameter is sufficient and can be found empirically with simulations. Finally, we demonstrate that our method readily scales to models of very high dimensionality, using toy models as well as by jointly inferring the effective population size, the distribution of fitness effects (DFE) of segregating mutations, and selection coefficients for each locus from data of a recent experiment on the evolution of drug resistance in influenza.

Mots-clé
Drug Resistance, Viral/genetics, Genetic Fitness, Genetic Loci, Models, Genetic, Mutation, Orthomyxoviridae/drug effects, Orthomyxoviridae/genetics, Probability, Selection, Genetic
Pubmed
Open Access
Oui
Création de la notice
16/05/2017 20:18
Dernière modification de la notice
21/08/2019 6:35
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