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 …

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 …

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 …

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 …

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

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 …

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of
underlying causes. However, determining which model parameters agree with complex and …

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

Mechanistic models of single-neuron dynamics have been extensively studied in
computational neuroscience. However, identifying which models can quantitatively …