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Local Probability Distributions in Bayesian Networks: Knowledge Elicitation and Inference

Zagorecki, Adam (2010) Local Probability Distributions in Bayesian Networks: Knowledge Elicitation and Inference. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowledge and have been applied successfully in many domains for over 25 years. The strength of Bayesian networks lies in the graceful combination of probability theory and a graphical structure representing probabilistic dependencies among domain variables in a compact manner that is intuitive for humans. One major challenge related to building practical BN models is specification of conditional probability distributions. The number of probability distributions in a conditional probability table for a given variable is exponential in its number of parent nodes, so that defining them becomes problematic or even impossible from a practical standpoint. The objective of this dissertation is to develop a better understanding of models for compact representations of local probability distributions. The hypothesis is that such models should allow for building larger models more efficiently and lead to a wider range of BN applications.

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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zagorecki, Adamzagorecki@gmail.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairZdzel, Marekmarek@sis.pitt.eduDRUZDZEL
Committee MemberCooper, Gregorygfc@pitt.eduGFC
Committee MemberLemmer, Johnjohn.lemmer@rl.af.mil
Committee MemberLewis, Michaelml@sis.pitt.eduCMLEWIS
Committee MemberFlynn, Rogerrflynn@sis.pitt.eduRFLYNN
Date: 17 May 2010
Date Type: Completion
Defense Date: 25 February 2010
Approval Date: 17 May 2010
Submission Date: 21 March 2010
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Bayesian networks; knowledge elicitation; noisy-OR
Other ID: http://etd.library.pitt.edu/ETD/available/etd-03212010-111246/, etd-03212010-111246
Date Deposited: 10 Nov 2011 19:32
Last Modified: 15 Nov 2016 13:37
URI: http://d-scholarship.pitt.edu/id/eprint/6542

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