An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs
提出一种更快的算法，用于生成多项逻辑模型下的贝叶斯最优设计，同时提高统计效率，并展示如何通过体育俱乐部会员研究来扩充选择设计。
While Bayesian G- and V-optimal designs for the multinomial logit model have been shown to have better predictive performance than Bayesian D- and A-optimal designs, the algorithms for generating them have been too slow for commercial use. In this article, we present a much faster algorithm for generating Bayesian optimal designs for all four criterial while simultaneously improving the statistical efficiency of the designs. We also show how to augment a choice design allowing for correlated parameter estimates using a sports club membership study.