Article
Likelihood-Free Inference in High-Dimensional Models.
Genetics - 1 Jun 2016
Kousathanas Athanasios, Leuenberger Christoph, Helfer Jonas, Quinodoz Mathieu, Foll Matthieu, Wegmann Daniel
Abstract excerpt
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...
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