Bayesian inference for complex regression models using mixtures

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The project will use mixtures to flexibly model complex regression functions and will develop Bayesian methods for carrying out statistical inference on these models. The models will deal with both Gaussian and non-Gaussian data. Multiple explanatory variables are dealt with by mixing simple additives to produce flexible high dimensional function estimates. Variable selection and model averaging will be used to identify important variables and thus make the estimation more efficient. The methods will be extended to multivariate responses where account will taken be taken of the structure of the dependence between responses.