Work

Bagging and the Bayesian Bootstrap

Year: 2001

Type: article

Abstract: Bagging is a method of obtaining more robust predictions when the model class under consideration is unstable with respect to the data, i.e., small changes in the data can cause the predicted values to change significantly. In this paper, we introduce a Bayesian version of bagging based on the Bayesian bootstrap. The Bayesian bootstrap resolves a theoretical problem with ordinary bagging and often results in more efficient estimators. We show how model averaging can be combined within the Bayesian bootstrap and illustrate the procedure with several examples. 1

Source: DukeSpace (Duke University)

Authors: Merlise A. Clyde, Herbert Lee

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: Model Uncertainty, Model Selection, and Robustness with Applications in Environmental Sciences, KDI: Multiscale Modeling and Simulation in Scientific Inference: Hierarchical Methods for Parameter Estimation in Porous Flow

OpenAlex ID: W2295947459

FWCI: 1.247

Cites: 14

Cited by: 34

Related to: 10