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Strong limit theorems for the bayesian scoring criterion in bayesian networks. / Slobodianik, Nikolai; Zaporozhets, Dmitry; Madras, Neal.

In: Journal of Machine Learning Research, Vol. 10, 01.07.2009, p. 1511-1526.

Research output: Contribution to journal › Article › peer-review

Harvard

Slobodianik, N, Zaporozhets, D & Madras, N 2009, 'Strong limit theorems for the bayesian scoring criterion in bayesian networks', Journal of Machine Learning Research, vol. 10, pp. 1511-1526.

APA

Slobodianik, N., Zaporozhets, D., & Madras, N. (2009). Strong limit theorems for the bayesian scoring criterion in bayesian networks. Journal of Machine Learning Research, 10, 1511-1526.

Vancouver

Slobodianik N, Zaporozhets D, Madras N. Strong limit theorems for the bayesian scoring criterion in bayesian networks. Journal of Machine Learning Research. 2009 Jul 1;10:1511-1526.

Author

Slobodianik, Nikolai ; Zaporozhets, Dmitry ; Madras, Neal. / Strong limit theorems for the bayesian scoring criterion in bayesian networks. In: Journal of Machine Learning Research. 2009 ; Vol. 10. pp. 1511-1526.

BibTeX

@article{5a8d5a3ad4244766a310d3016e5c4198,
title = "Strong limit theorems for the bayesian scoring criterion in bayesian networks",
abstract = "In the machine learning community, the Bayesian scoring criterion is widely used for model selection problems. One of the fundamental theoretical properties justifying the usage of the Bayesian scoring criterion is its consistency. In this paper we refine this property for the case of binomial Bayesian network models. As a by-product of our derivations we establish strong consistency and obtain the law of iterated logarithm for the Bayesian scoring criterion. {\textcopyright} 2009 Nikolai Slobodianik, Dmitry Zaporozhets and Neal Madras.",
keywords = "Bayesian networks, BIC, Consistency, Model selection, Scoring criterion",
author = "Nikolai Slobodianik and Dmitry Zaporozhets and Neal Madras",
year = "2009",
month = jul,
day = "1",
language = "English",
volume = "10",
pages = "1511--1526",
journal = "Journal of Machine Learning Research",
issn = "1532-4435",
publisher = "Microtome Publishing",

}

RIS

TY - JOUR

T1 - Strong limit theorems for the bayesian scoring criterion in bayesian networks

AU - Slobodianik, Nikolai

AU - Zaporozhets, Dmitry

AU - Madras, Neal

PY - 2009/7/1

Y1 - 2009/7/1

N2 - In the machine learning community, the Bayesian scoring criterion is widely used for model selection problems. One of the fundamental theoretical properties justifying the usage of the Bayesian scoring criterion is its consistency. In this paper we refine this property for the case of binomial Bayesian network models. As a by-product of our derivations we establish strong consistency and obtain the law of iterated logarithm for the Bayesian scoring criterion. © 2009 Nikolai Slobodianik, Dmitry Zaporozhets and Neal Madras.

AB - In the machine learning community, the Bayesian scoring criterion is widely used for model selection problems. One of the fundamental theoretical properties justifying the usage of the Bayesian scoring criterion is its consistency. In this paper we refine this property for the case of binomial Bayesian network models. As a by-product of our derivations we establish strong consistency and obtain the law of iterated logarithm for the Bayesian scoring criterion. © 2009 Nikolai Slobodianik, Dmitry Zaporozhets and Neal Madras.

KW - Bayesian networks

KW - BIC

KW - Consistency

KW - Model selection

KW - Scoring criterion

UR - http://www.scopus.com/inward/record.url?scp=68949147860&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:68949147860

VL - 10

SP - 1511

EP - 1526

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

SN - 1532-4435

ER -

ID: 126290389