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- Publisher
- Springer Journals
- Copyright
- Copyright © 2018 by Classification Society of North America
- Subject
- Statistics; Statistical Theory and Methods; Pattern Recognition; Bioinformatics; Signal,Image and Speech Processing; Psychometrics; Marketing
- ISSN
- 0176-4268
- eISSN
- 1432-1343
- DOI
- 10.1007/s00357-018-9248-z
- Publisher site
- See Article on Publisher Site
Abstract
This paper discusses the challenges presented by tall data problems associated with Bayesian classification (specifically binary classification) and the existing methods to handle them. Current methods include parallelizing the likelihood, subsampling, and consensus Monte Carlo. A new method based on the two-stage Metropolis-Hastings algorithm is also proposed. The purpose of this algorithm is to reduce the exact likelihood computational cost in the tall data situation. In the first stage, a new proposal is tested by the approximate likelihood based model. The full likelihood based posterior computation will be conducted only if the proposal passes the first stage screening. Furthermore, this method can be adopted into the consensus Monte Carlo framework. The two-stage method is applied to logistic regression, hierarchical logistic regression, and Bayesian multivariate adaptive regression splines.
Journal
Journal of Classification – Springer Journals
Published: Mar 16, 2018