The key processes in biological and chemical systems are described by networks of
chemical reactions. From molecular biology to biotechnology applications, computational …

The literature on agent-based models has been highly successful in replicating many
stylized facts of financial and macroeconomic time series. Over the past decade, however …

This paper takes the reader on a journey through the history of Bayesian computation, from
the 18th century to the present day. Beginning with the one-dimensional integral first …

When conducting Bayesian inference, delayed-acceptance (DA) Metropolis–Hastings (MH)
algorithms and DA pseudo-marginal MH algorithms can be applied when it is …

The Bayesian statistical paradigm uses the language of probability to express uncertainty
about the phenomena that generate observed data. Probability distributions thus …

MCMC algorithms such as Metropolis-Hastings algorithms are slowed down by the
computation of complex target distributions as exemplified by huge datasets. We offer in this …

This survey gives an overview of Monte Carlo methodologies using surrogate models, for
dealing with densities that are intractable, costly, and/or noisy. This type of problem can be …

Abstract Models of stochastic processes are widely used in almost all fields of science.
Theory validation, parameter estimation, and prediction all require model calibration and …

The past few decades have witnessed an explosive synergy between physics and the life
sciences. In particular, physical modelling in medicine and physiology is a topical research …

The complexity of the Metropolis–Hastings (MH) algorithm arises from the requirement of a
likelihood evaluation for the full dataset in each iteration. One solution has been proposed to …