Bayesian choice modelling

离散选择模型很重要，因为它们提供了帮助理解决策者选择过程的工具。指定具有足够灵活的协方差结构的模型，以捕获选择之间交叉替代的复杂模式，同时能够捕获个体行为中存在的异质性，这仍然是一个挑战。我们将开发一种贝叶斯方法来使用协方差选择来克服这些问题。这将培训研究人员，并提高澳大利亚在社会科学中重要的活跃研究领域的形象；实质性应用将在卫生经济学中，但发展也将与生物统计学、流行病学和生态学等相关领域有关。

Discrete choice models are important as they provide tools to help understand choice processes of decision makers. It remains a challenge to specify models with covariance structures flexible enough to capture complex patterns of cross-substitution between choices while being able to capture heterogeneity present in individual behaviour. We will develop a Bayesian approach to choice modelling that uses covariance selection to overcome these problems. This will train researchers and raise the profile of Australia in an active research area that is important in the social sciences; substantive applications will be in health economics, but developments will also be relevant to cognate areas of biostatistics, epidemiology, and ecology.