Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2013-01, Vol.43 (1), p.186-195
Main Authors: Zagorecki, A., Druzdzel, M. J.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Bayesian analysis
Bayesian networks
Computer science
control theory
systems
Cybernetics
Engines
Exact sciences and technology
Gates
Information retrieval. Graph
Information systems. Data bases
Inhibitors
knowledge elicitation
Knowledge engineering
Logic gates
Mathematical model
Mathematical models
Memory organisation. Data processing
Networks
Noise measurement
noisy-MAX
noisy-OR
Probability distribution
Software
Software engineering
Studies
Tables
Theoretical computing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMCA.2012.2189880