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Knowledge engineering for bayesian networks: How common are noisy-MAX distributions in practice'

Zagorecki, A and Druzdzel, MJ (2013) Knowledge engineering for bayesian networks: How common are noisy-MAX distributions in practice'. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 43 (1). 186 - 195. ISSN 1083-4427

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Abstract

One problem faced in knowledge engineering for Bayesian networks (BNs) is the exponential growth of the number of parameters in their conditional probability tables (CPTs). The most common practical solution is the application of the so-called canonical gates and, among them, the noisy-OR (or their generalization, the noisy-MAX) gates, which take advantage of the independence of causal interactions and provide a logarithmic reduction of the number of parameters required to specify a CPT. In this paper, we propose an algorithm that fits a noisy-MAX distribution to an existing CPT, and we apply this algorithm to search for noisy-MAX gates in three existing practical BN models: ALARM, HAILFINDER, and HEPAR II. We show that the noisy-MAX gate provides a surprisingly good fit for as many as 50% of CPTs in two of these networks. We observed this in both distributions elicited from experts and those learned from data. The importance of this finding is that it provides an empirical justification for the use of the noisy-MAX gate as a powerful knowledge engineering tool. © 2013 IEEE.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zagorecki, A
Druzdzel, MJmarek@sis.pitt.eduDRUZDZEL0000-0002-7598-2286
Date: 1 January 2013
Date Type: Publication
Journal or Publication Title: IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Volume: 43
Number: 1
Page Range: 186 - 195
DOI or Unique Handle: 10.1109/tsmca.2012.2189880
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 1083-4427
Date Deposited: 25 Jun 2013 16:31
Last Modified: 05 Mar 2019 01:55
URI: http://d-scholarship.pitt.edu/id/eprint/19101

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