RT Journal Article
SR Electronic
T1 Likelihood-Free Inference in High-Dimensional Models
JF Genetics
JO Genetics
FD Genetics Society of America
SP 893
OP 904
DO 10.1534/genetics.116.187567
VO 203
IS 2
A1 Kousathanas, Athanasios
A1 Leuenberger, Christoph
A1 Helfer, Jonas
A1 Quinodoz, Mathieu
A1 Foll, Matthieu
A1 Wegmann, Daniel
YR 2016
UL http://www.genetics.org/content/203/2/893.abstract
AB 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.