Two related developments are currently changing traditional approaches to computational
systems biology modelling. First, stochastic models are being used increasingly in …

Mathematical models are routinely calibrated to experimental data, with goals ranging from
building predictive models to quantifying parameters that cannot be measured. Whether or …

Summary Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as
the two main tools to sample from high dimensional probability distributions. Although …

Since the first edition of Stochastic Modelling for Systems Biology, there have been many
interesting developments in the use of" likelihood-free" methods of Bayesian inference for …

Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main
tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov …

The interplay of mathematical modelling with experiments is one of the central elements in
systems biology. The aim of reverse engineering is to infer, analyse and understand …

Bayesian methods are valuable, inter alia, whenever there is a need to extract information
from data that are uncertain or subject to any kind of error or noise (including measurement …

The ability to infer parameters of gene regulatory networks is emerging as a key problem in
systems biology. The biochemical data are intrinsically stochastic and tend to be observed …

Monte Carlo methods, in particular those based on Markov chains and on interacting particle
systems, are by now tools that are routinely used in machine learning. These methods have …

This paper provides an optimal filtering methodology in discretely observed continuous-time
jump-diffusion models. Although the filtering problem has received little attention, it is useful …