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Explaining inference on a population of independent agents using Bayesian networks

Sutovsky, Peter (2013) Explaining inference on a population of independent agents using Bayesian networks. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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The main goal of this research is to design, implement, and evaluate a novel explanation method, the hierarchical explanation method (HEM), for explaining Bayesian network (BN) inference when the network is modeling a population of conditionally independent agents, each of which is modeled as a subnetwork. For example, consider disease-outbreak detection in which the agents are patients who are modeled as independent, conditioned on the factors that cause disease spread. Given evidence about these patients, such as their symptoms, suppose that the BN system infers that a respiratory anthrax outbreak is highly likely. A public-health official who received such a report would generally want to know why anthrax is being given a high posterior probability. The HEM explains such inferences. The explanation approach is applicable in general to inference on BNs that model conditionally independent agents; it complements previous approaches for explaining inference on BNs that model a single agent (e.g., for explaining the diagnostic inference for a single patient using a BN that models just that patient). The hypotheses that were tested are: (1) the proposed explanation method provides information that helps a user to understand how and why the inference results have been obtained, (2) the proposed explanation method helps to improve the quality of the inferences that users draw from evidence.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairCooper, Gregorygfc@pitt.eduGFC
Committee CoChairDruzdzel, Marek J.marek@sis.pitt.eduDRUZDZEL
Committee MemberFlynn, Roger R.rflynn@sis.pitt.eduRFLYNN
Committee MemberHauskrecht, Milosmilos@cs.pitt.eduMILOS
Committee MemberLewis, Michaelml@sis.pitt.eduCMLEWIS
Date: 27 August 2013
Date Type: Publication
Defense Date: 29 July 2013
Approval Date: 27 August 2013
Submission Date: 20 August 2013
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 148
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: explanation of inference, Bayesian networks, biosurveillance, disease outbreak detection
Date Deposited: 27 Aug 2013 19:24
Last Modified: 15 Nov 2016 14:15


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