On the Application of The Bootstrap for Computing Confidence Measures on Features of Induced Bayesian Networks

Authors:

Nir Friedman

Institute of Computer Science

Hebrew University

Givat Ram

Jerusalem 91904, ISRAEL

E-mail: nir@cs.huji.ac.il

Phone: +972-2-658-4720

Fax: +972-2-658-5439

Moises Goldszmidt

SRI International

333 Ravenswood Ave

Menlo Park CA 94025

E-mail: moises@erg.sri.com

Phone: 650-859-4319

Fax: 650-859-4812

Abraham Wyner

Department of Statistics

Wharton School

University of Pennsylvania

Philadelphia, PA 19104

E-mail: ajw@stat.wharton.upenn.edu

Phone: 215-898-2439

Fax: 215-898-1280

Abstract:

In the context of learning Bayesian networks from data, very little work has been published on methods for assessing the quality of an induced model. This issue, however, has received a great deal of attention in the statistics literature. In this paper, we take a well-known method from statistics, Efron's Bootstrap, and examine its applicability for assessing a confidence measure on features of the learned network structure. We also compare this method to assessments based on a practical realization of the Bayesian methodology.

On the Application of The Bootstrap for Computing Confidence Measures on Features of Induced Bayesian Networks

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