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 …

Stochasticity is a key characteristic of intracellular processes such as gene regulation and
chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is …

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 decades have seen enormous improvements in computational inference for
statistical models; there have been competitive continual enhancements in a wide range of …

We consider Bayesian inference techniques for agent-based (AB) models, as an alternative
to simulated minimum distance (SMD). Three computationally heavy steps are involved:(i) …

Likelihood-based inference for disease outbreak data can be very challenging due to the
inherent dependence of the data and the fact that they are usually incomplete. In this paper …

Bayesian inference is often used in cosmology and astrophysics to derive constraints on
model parameters from observations. This approach relies on the ability to compute the …

This chapter adds coverage of recent developments, particularly on auxiliary likelihood
methods and ABC model choice. It focuses on summary statistic selection methods which …

We consider Bayesian inference problems with computationally intensive likelihood
functions. We propose a Gaussian process (GP)–based method to approximate the joint …

Managing the uncertainty in multidisciplinary design of safety-critical systems requires not
only the availability of a single approach or methodology to deal with uncertainty but a set of …