PT - JOURNAL ARTICLE
AU - Kousathanas, Athanasios
AU - Leuenberger, Christoph
AU - Helfer, Jonas
AU - Quinodoz, Mathieu
AU - Foll, Matthieu
AU - Wegmann, Daniel
TI - Likelihood-Free Inference in High-Dimensional Models
AID - 10.1534/genetics.116.187567
DP - 2016 Jun 01
TA - Genetics
PG - 893--904
VI - 203
IP - 2
4099 - http://www.genetics.org/content/203/2/893.short
4100 - http://www.genetics.org/content/203/2/893.full
SO - Genetics2016 Jun 01; 203
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.