Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy

• 成果类型: Article
• 署名作者: Linero, Antonio R.
• 署名单位: University of Texas System; University of Texas Austin
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2337156
• 发表日期: 2025
• 页码: 356-369
• 关键词: computation; inference; binary

摘要

Bayesian additive regression trees have seen increased interest in recent years due to their ability to combine machine learning techniques with principled uncertainty quantification. The Bayesian backfitting algorithm used to fit BART models, however, limits their application to a small class of models for which conditional conjugacy exists. In this article, we greatly expand the domain of applicability of BART to arbitrary generalized BART models by introducing a very simple, tuning-parameter-free, reversible jump Markov chain Monte Carlo algorithm. Our algorithm requires only that the user be able to compute the likelihood and (optionally) its gradient and Fisher information. The potential applications are very broad; we consider examples in survival analysis, structured heteroscedastic regression, and gamma shape regression. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.