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 …

Generative adversarial networks (GANs) provide an algorithmic framework for constructing
generative models with several appealing properties: they do not require a likelihood …

A growing number of generative statistical models do not permit the numerical evaluation of
their likelihood functions. Approximate Bayesian computation has become a popular …

Having the ability to work with complex models can be highly beneficial. However, complex
models often have intractable likelihoods, so methods that involve evaluation of the …

Abstract Motivation Approximate Bayesian computation (ABC) has grown into a standard
methodology that manages Bayesian inference for models associated with intractable …

Building on a strong foundation of philosophy, theory, methods and computation over the
past three decades, Bayesian approaches are now an integral part of the toolkit for most …

An explosion of high-throughput DNA sequencing in the past decade has led to a surge of
interest in population-scale inference with whole-genome data. Recent work in population …

We analyse the behaviour of approximate Bayesian computation (ABC) when the model
generating the simulated data differs from the actual data-generating process, ie when the …

A growing range of generative statistical models are such the numerical evaluation of their
likelihood functions is intractable. Approximate Bayesian computation and indirect inference …

We present extensive empirical evidence showing that current Bayesian simulation-based
inference algorithms can produce computationally unfaithful posterior approximations. Our …