Variational Inference for Large-Scale Models of Discrete Choice

• 成果类型: Article
• 署名作者: Braun, Michael; McAuliffe, Jon
• 署名单位: Massachusetts Institute of Technology (MIT); University of California System; University of California Berkeley
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.tm08030
• 发表日期: 2010
• 页码: 324-335
• 关键词: consumer choice

摘要

Discrete choice models are commonly used by applied statisticians in numerous fields. such as marketing. economics. finance. and operations research When agents in discrete choice models are assumed to have differing preferences. exact inference is often intractable Markov chain Monte Carlo techniques make approximate inference possible. but the computational cos is prohibitive on the large damsels now becoming untimely available Variational I methods provide a deterministic alternative for approximation of the posterior distribution We derive variational procedures for empirical Bayes and fully Bayesian inference in the mixed multinomial logit model of discrete choice The algorithms require only that we solve a sequence of unconstrained optimization problems. which are shown to be convex One version (0 the procedures relies on a new approximation to the variational objective function. based on the multivariate delta method Extensive simulations. along with an analysis of real-world data, demonstrate that variational methods achieve accuracy competitive with Markov chain Monte Carlo at a small fraction of the computational cost Thus. variational methods permit inference on damsels that otherwise cannot be analyzed without possibly adverse simplifications of the underlying discrete choice model Appendices C through F are available as online supplemental materials