ABSTRACT

The field of spatio-temporal epidemiology has expanded rapidly in the past 10 years due to the development of statistical techniques that can accommodate variation over both space and time and the increasing availability of high-resolution data measuring a wide variety of environmental processes. Bayesian hierarchical modelling has steadily expanded as has the ability to handle a large number (n) of measurement vectors which may be of high dimension (p). Conventional methods for performing Bayesian analysis may be infeasible due to their high computational demands, paving the way for approximate methods for Bayesian inference such as INLA (see Chapter 5, Section 5.6).