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 …

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 …

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 …

During reionization, neutral hydrogen in the intergalactic medium (IGM) imprints a damping
wing absorption feature on the spectrum of high-redshift quasars. A detection of this …

We measure the persuasive effects of slanted news and tastes for like-minded news,
exploiting cable channel positions as exogenous shifters of cable news viewership. Channel …

Provides a comprehensive and updated study of GARCH models and their applications in
finance, covering new developments in the discipline This book provides a comprehensive …

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 …

No subject with a foot in both the academic and public domains like macroeconomics
remains unchanged for long. The search for improved explanations, and the challenge of …

Monte Carlo statistical methods, particularly those based on Markov chains, are now an
essential component of the standard set of techniques used by statisticians. This new edition …