Many of the statistical models that could provide an accurate, interesting, and testable
explanation for the structure of a data set turn out to have intractable likelihood functions …

Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in
many other branches of science. It provides a principled framework for dealing with …

As the world becomes increasingly complex, so do the statistical models required to analyse
the challenging problems ahead. For the very first time in a single volume, the Handbook of …

Abstract We present Sequential Neural Likelihood (SNL), a new method for Bayesian
inference in simulator models, where the likelihood is intractable but simulating data from …

Approximate Bayesian computation (ABC) constitutes a class of computational methods
rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function …

Abstract Approximate Bayesian Computation (ABC) methods, also known as likelihood-free
techniques, have appeared in the past ten years as the most satisfactory approach to …

Many recent statistical applications involve inference under complex models, where it is
computationally prohibitive to calculate likelihoods but possible to simulate data …

Abstract Motivation: Approximate Bayesian computation (ABC) methods provide an
elaborate approach to Bayesian inference on complex models, including model choice. Both …

Our paper deals with inferring simulator-based statistical models given some observed data.
A simulator-based model is a parametrized mechanism which specifies how data are …

Many modern statistical applications involve inference for complex stochastic models, where
it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate …