Bayesian inference for complex regression models using mixtures

该项目将使用混合物来灵活地模拟复杂的回归函数，并将开发贝叶斯方法来对这些模型进行统计推断。这些模型将处理高斯和非高斯数据。多个解释变量的处理混合简单的添加剂，以产生灵活的高维函数估计。将使用变量选择和模型平均值来确定重要变量，从而使估计更加有效。该方法将被扩展到多变量响应，其中将考虑响应之间的依赖结构。

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.