Andrew Golightly (Newcastle University)

Auxiliary particle MCMC schemes for partially observed Markov jump processes

We consider Bayesian inference for parameters governing Markov jump processes (MJPs) using discretely observed data that may be incomplete and subject to measurement error. We use a recently proposed particle MCMC scheme which jointly updates parameters of interest and the latent process and present a vanilla implementation based on a bootstrap filter before considering improvements based around an auxiliary particle filter. In particular, we focus on a linear noise approximation to the MJP to construct a pre-weighting scheme and couple this with a bridging mechanism. Finally, we embed this approach within a 'delayed acceptance' framework to allow further computational gains. The methods are illustrated with some examples arising in systems biology.

Peter Challenor (Exeter)

The Statistical Analysis of Complex Numerical Models in the Environmental Sciences

Complex, usually deterministic, numerical models are now widely used in environmental science. Predictions from such models are normally presented without any measure of uncertainty. In this talk I shall show how statistical methods can be used to not only estimate the uncertainty on model predictions but also to carry out sensitivity analysis and to calibrate the models. Our basic tool is the emulator. The emulator is a statistical approximation to the full numerical simulator; we use a Gaussian process. We build the emulator from an initial designed set of model runs. Because it is fast to run and we know its statistical properties, the emulator we can be used for inference, either analytically or using Monte Carlo methods. Drawing on examples in climate and oceanography I will illustrate the construction and use of statistical emulators from the design of experiments to the estimation of extreme values.

Kevin Korb (Monash)

An Overview of Bayesian Network Research at Monash

Recent research on and around Bayesian net (BN) technology at Monash has featured: fog forecasting (the Bureau of Meteorology); environmental management (Victorian gov); biosecurity (ACERA); finding better discretizations, using cost-based data and Bayesian scoring rules; data mining dynamic Bayesian networks; re-activating MML causal discovery of linear models. I'll discuss these and some other BN activities, briefly describe other research happening at Monash FIT, and the opportunities for collaboration.