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 …

Motivation: DIYABC is a software package for a comprehensive analysis of population
history using approximate Bayesian computation on DNA polymorphism data. Version 2.0 …

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 …

The identification of population bottlenecks is critical in conservation because populations
that have experienced significant reductions in abundance are subject to a variety of genetic …

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 …