Understanding the forces that influence natural variation within and among populations has
been a major objective of evolutionary biologists for decades. Motivated by the growth in …

In the past 10years a statistical technique, approximate Bayesian computation (ABC), has
been developed that can be used to infer parameters and choose between models in the …

Stochastic simulation is a key tool in population genetics, since the models involved are
often analytically intractable and simulation is usually the only way of obtaining ground-truth …

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 …

Bayesian Statistics: Page 1 Bayesian Statistics: An Introduction PETER M. LEE Formerly Provost
of Wentworth College, University of York, England Fourth Edition John Wiley & Sons, Ltd Page …

Recent advances in probabilistic modelling have led to a large number of simulation-based
inference algorithms which do not require numerical evaluation of likelihoods. However, a …

As population genomic datasets grow in size, researchers are faced with the daunting task
of making sense of a flood of information. To keep pace with this explosion of data …

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 …

How can one perform Bayesian inference on stochastic simulators with intractable
likelihoods? A recent approach is to learn the posterior from adaptively proposed …

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