Moment conditions and Bayesian non-parametrics

• 成果类型: Article
• 署名作者: Bornn, Luke; Shephard, Neil; Solgi, Reza
• 署名单位: Simon Fraser University; Harvard University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12294
• 发表日期: 2019
• 页码: 5-43
• 关键词: empirical likelihood; molecular-dynamics; generalized-method; sample properties; free-energy; computation; inference; models; algorithm

摘要

Models phrased through moment conditions are central to much of modern inference. Here these moment conditions are embedded within a non-parametric Bayesian set-up. Handling such a model is not probabilistically straightforward as the posterior has support on a manifold. We solve the relevant issues, building new probability and computational tools by using Hausdorff measures to analyse them on real and simulated data. These new methods, which involve simulating on a manifold, can be applied widely, including providing Bayesian analysis of quasi-likelihoods, linear and non-linear regression, missing data and hierarchical models.