Bayesian mixture modelling for characterising environmental exposures and outcomes
Darren Wraith · 2008
Environmental exposure and outcomes assessment is a great challenge to scientists. Increasingly more and more detailed data are becoming available to understand the nature and complexity of the relationships involved. The methodology of mixture models provides a means to understand, quantify and describe features and relation- ships within complex data sets. In this thesis, we focussed on a number of applied problems to characterise complex environmental exposure and outcomes, including: assessing the interaction between environmental exposures as risk factors for health outcomes; identifying di®ering environmental outcomes across a region; and estab- lishing patterns in the size and concentration of aerosol particles over time. Mixture model approaches to address these problems are developed and examined for their suitability in these contexts.