Stochastic process models in one and two dimensions are increasingly important in applied modelling of real-world data sets in finance, epidemic and infectious disease modelling and other health-data analysis.  In this workshop we will study the Bayesian analysis of data for which such stochastic process models are widely deemed to be appropriate. Building on methods developed for non-stochastic (ODE) models, we will illustrate the use of Markov chain Monte Carlo (MCMC) approaches for the analysis of stochastic process models driven by Brownian motion but also more a complicated driving process, the Levy process.  Methods studied will include those based on linear noise approximation, particle methods and (where possible) exact Bayesian computation. We will also study Bayesian methods and MCMC approaches to spatial (point process) data.

This Workshop will be in person but, due to unforeseen circumstances, the presenter (Prof. David Stephens) of this Workshop will be virtual.  He will have his PhD student on site to coordinate the delivery of the lectures.