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Application of Bayesian Networks to Risk Assessment

Kraisangka, Jidapa (2019) Application of Bayesian Networks to Risk Assessment. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Various approaches are used to estimate and predict risks. One of the most prevalent methods for risk assessment is the Cox's proportional hazard (CPH) model (Cox, 1972), a popular statistical technique used in risk estimation and survival analysis. The weaknesses of this approach are: (1) the underlying model can be only learned from data and is not readily amenable to refinement based on expert knowledge (2) the CPH model rests on several assumptions simplifying the interactions between the risk factors and the predicted outcome.
While these assumptions are reasonable and the CPH model has been successfully used for decades, it is interesting to question them with a possible benefit in terms of model accuracy.

This dissertation focuses on theoretical and practical aspects of risk assessment based on Bayesian networks (Pearl, 1988) as an alternative approach to the CPH model. The dissertation makes three contributions: (1) I propose a Bayesian network interpretation of the CPH (BN-Cox) model, a process of using existing CPH models as data sources for parameter estimation in Bayesian networks when original data are not available, and discuss methods for modeling such model computationally tractable (2) I empirically demonstrate in both context-sensitivity of the strength of influences of individual risk factors on the outcome variables in both Bayesian network model and the CPH model, and finally, (3) I propose and evaluate methods for enhancing the quality of Bayesian network parameters learned from small data sets, by means of priors.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Kraisangka, Jidapajik41@pitt.edujik41
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairDruzdzel,
Committee MemberHirtle,
Committee MemberMunro,
Committee MemberBenza,
Date: 22 May 2019
Date Type: Publication
Defense Date: 10 April 2019
Approval Date: 22 May 2019
Submission Date: 18 April 2019
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 83
Institution: University of Pittsburgh
Schools and Programs: School of Computing and Information > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Bayesian networks, Risk assessment
Date Deposited: 22 May 2019 12:32
Last Modified: 22 May 2020 05:15

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