Bayesian Ensemble Learning
Loading...
Penn collection
Statistics Papers
Degree type
Discipline
Subject
Statistics and Probability
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Chipman, Hugh A
George, Edward I
McCulloch, Robert E
Contributor
Abstract
We develop a Bayesian “sum-of-trees” model, named BART, where each tree is constrained by a prior to be a weak learner. Fitting and inference are accomplished via an iterative backfitting MCMC algorithm. This model is motivated by ensemble methods in general, and boosting algorithms in particular. Like boosting, each weak learner (i.e., each weak tree) contributes a small amount to the overall model. However, our procedure is defined by a statistical model: a prior and a likelihood, while boosting is defined by an algorithm. This model-based approach enables a full and accurate assessment of uncertainty in model predictions, while remaining highly competitive in terms of predictive accuracy.
Advisor
Date of presentation
2006-01-01
Conference name
Statistics Papers
Conference dates
2023-05-17T15:04:46.000