Scalable Bayesian Nonparametric Clustering and Classification. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Ni, Yang; Müller, Peter; Diesendruck, Maurice; Williamson, Sinead; Zhu, Yitan; Ji, Yuan
• Is Part Of: Journal of computational and graphical statistics. Volume 29:Issue 1(2020)
• Page Start: 53
• Page End: 65
• Publication Date: 2020-01-02
• Subjects: Electronic health records -- Nonconjugate models -- Parallel computing -- Product partition models; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2019.1624366
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract: We develop a scalable multistep Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is "embarrassingly parallel" and can be implemented using the same Markov chain Monte Carlo sampler. The simplicity and generality of our approach make inference for a wide range of Bayesian nonparametric mixture models applicable to large datasets. Specifically, we apply the approach to inference under a product partition model with regression on covariates. We show results for inference with two motivating datasets: a large set of electronic health records and a bank telemarketing dataset. We find interesting clusters and competitive classification performance relative to other widely used competing classifiers. Supplementary materials for this article are available online.