Modelling preference heterogeneity using a Bayesian finite mixture of Almost Ideal Demand Systems
用贝叶斯有限混合几乎理想需求系统来捕捉偏好异质性，利用5类食品的截断购买数据估计出四种偏好类别，并分析类别差异及驱动分组的食品类别。
Abstract Demand studies often use observable characteristics to proxy preference heterogeneity. It is likely, however, that some households with the same observable characteristics have quite different preferences. An alternative approach is to use a Gaussian mixture of Almost Ideal Demand Systems to capture the heterogeneity. We show how to estimate this with censored purchase data for 5 food categories using Bayesian inference. Using model outputs we infer four different preference classes; how distinct these classes are from one another and which food categories are driving the segmentation process.