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The prediction of posttraumatic stress disorder (PTSD) has gained a lot of interest in clinical studies. Identifying patients with a high risk of PTSD can guide mental healthcare workers when making treatment decisions. The main goal of this paper is to propose several Bayesian network (BN) models to assess the probability that a veteran has PTSD when first visiting a U.S. Department of Veteran Affairs (VA) facility seeking medical care. The current practice is to use a five-question test called PC-PTSD-5. We aim to use the PC-PTSD-5 test, which is currently administered to most incoming new patients, and demographic information, military service history, and medical history. We construct a Bayes information criterion score-based BN, a group L2-regularized BN (GL2-regularized BN), and a naïve Bayes BN to assess the probability that a patient has PTSD. The GL2-regularized BN is a new method for constructing a BN motivated by some of the challenges of analyzing this data set. A secondary goal is to identify which features are important in predicting PTSD. We discover that the following features help compute the probability of PTSD: PC-PTSD-5, service-connected flag, combat flag, agent orange flag, military sexual trauma flag, traumatic brain injury, and age. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0174) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0174). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2006.04.019 Byline: Lili Sun (a), Prakash P. Shenoy (b) Keywords: Bankruptcy prediction; Bayesian networks; NaA[macron]ve Bayes; Variable selection; Discretization of continuous variables Abstract: This study provides operational guidance for building naA[macron]ve Bayes Bayesian network (BN) models for bankruptcy prediction. First, we suggest a heuristic method that guides the selection of bankruptcy predictors. Based on the correlations and partial correlations among variables, the method aims at eliminating redundant and less relevant variables. A naA[macron]ve Bayes model is developed using the proposed heuristic method and is found to perform well based on a 10-fold validation analysis. The developed naA[macron]ve Bayes model consists of eight first-order variables, six of which are continuous. We also provide guidance on building a cascaded model by selecting second-order variables to compensate for missing values of first-order variables. Second, we analyze whether the number of states into which the six continuous variables are discretized has an impact on the model's performance. Our results show that the model's performance is the best when the number of states for discretization is either two or three. Starting from four states, the performance starts to deteriorate, probably due to over-fitting. Finally, we experiment whether modeling continuous variables with continuous distributions instead of discretizing them can improve the model's performance. Our finding suggests that this is not true. One possible reason is that continuous distributions tested by the study do not represent well the underlying distributions of empirical data. Finally, the results of this study could also be applicable to business decision-making contexts other than bankruptcy prediction. Author Affiliation: (a) Accounting and Information Systems, Rutgers, The State University of New Jersey, 180 University Ave, Newark, NJ 07102-1897, USA (b) School of Business, University of Kansas, 1300 Sunnyside Ave, Summerfield Hall, Lawrence, KS 66045-7585, USA Article History: Received 2 March 2005; Accepted 19 April 2006

This paper proposes a linear belief function (LBF) approach to evaluate portfolio performance. By drawing on the notion of LBFs, an elementary approach to knowledge representation in expert systems is proposed. It is shown how to use basic matrices to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, distributional assumptions, linear relations, and empirical asset-pricing models. The authors then appeal to Dempster's rule of combination to integrate the knowledge for assessing the overall belief of portfolio performance and updating the belief by incorporating additional evidence. An example of three gold stocks is used to illustrate the approach. Index Terms--Dempster-Shafer belief functions, knowledge-based systems, linear belief functions, multivariate normal distribution, portfolio evaluation.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.06.009 Byline: Riza Demirer (a), Prakash P. Shenoy (b) Keywords: Decision analysis; Sequential decision diagrams; Valuation networks; Asymmetric decision problems; Influence diagrams Abstract: This paper deals with representation and solution of asymmetric decision problems. We describe a new representation called sequential valuation networks that is a hybrid of Covaliu and Oliver's sequential decision diagrams and Shenoy's valuation networks. The solution algorithm is based on the idea of decomposing a large asymmetric problem into smaller sub-problems and then using the fusion algorithm of valuation networks to solve the sub-problems. Sequential valuation networks inherit many of the strengths of sequential decision diagrams and valuation networks while overcoming many of their shortcomings. We illustrate our technique by representing and solving a modified version of Covaliu and Oliver's [Manage. Sci. 41(12) (1995) 1860] Reactor problem in complete detail. Author Affiliation: (a) Department of Economics and Finance, Southern Illinois University--Edwardsville, Alumni Hall, Edwardsville, IL 62026-1102, USA (b) School of Business, University of Kansas, 1300 Sunnyside Avenue, Summerfield Hall, Lawrence, KS 66045-7585, USA Article History: Received 8 June 2003; Accepted 24 June 2004

A utility theory useful for decision-making in the presence of uncertainty that is assessed by possibility theory is suggested.

The main contribution of this paper is a new definition of expected value of belief functions in the Dempster–Shafer (D–S) theory of evidence. Our definition shares many of the properties of the expectation operator in probability theory. Also, for Bayesian belief functions, our definition provides the same expected value as the probabilistic expectation operator. A traditional method of computing expected of real-valued functions is to first transform a D–S belief function to a corresponding probability mass function, and then use the expectation operator for probability mass functions. Transforming a belief function to a probability function involves loss of information. Our expectation operator works directly with D–S belief functions. Another definition is using Choquet integration, which assumes belief functions are credal sets, i.e. convex sets of probability mass functions. Credal sets semantics are incompatible with Dempster's combination rule, the center-piece of the D–S theory. In general, our definition provides different expected values than, e.g. if we use probabilistic expectation using the pignistic transform or the plausibility transform of a belief function. Using our definition of expectation, we provide new definitions of variance, covariance, correlation, and other higher moments and describe their properties. [ABSTRACT FROM AUTHOR]