School of Business Scholarly Works: Recent submissions
Now showing items 161-180 of 216
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On the plausibility transformation method for translating belief function models to probability models
(Elsevier, 2006-04)In this paper, we propose the plausibility transformation method for translating Dempster-Shafer (D-S) belief function models to probability models, and describe some of its properties. There are many other transformation ... -
Inference in hybrid Bayesian networks wih mixtures of truncated exponenials
(Elsevier, 2006-04)Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated with an MTE potential, which can ... -
Does your ad have too many pictures?
(Cambridge University Press, 2000-01)This paper reports findings from a study that evaluates the effectiveness of longer print advertisements-the advertisements with a low copy-picture ratio (i.e., primarily pictorial advertisements or PPAs). Move specifically, ... -
Approximating Probability Density Functions with Mixtures of Truncated Exponentials
(2004-07)Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for approximating probability density functions (PDF’s). This paper presents MTE potentials that approximate standard PDF’s and ... -
Decision making on the sole basis of statistical likelihood
(Elsevier Science Publishers B. V., 2005-07)This paper presents a new axiomatic decision theory for choice under uncertainty. Unlike Bayesian decision theory where uncertainty is represented by a probability function, in our theory, uncertainty is given in the form ... -
Sequential valuation networks for asymmetric decision problems
(Elsevier Science Publishers B. V., 2006-02-16)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 ... -
Probability Propagation
(Annals of Mathematics and Artificial Intelligence, 1990-03)In this paper we give a simple account of local computation of marginal probabilities for when the joint probability distribution is given in factored form and the sets of variables involved in the factors form a hypertree. ... -
Hybrid Bayesian Networks with Linear Deterministic Variables
(Association for Uncertainty in Artificial Intelligence, 2005-07)When a hybrid Bayesian network has conditionally deterministic variables with continuous parents, the joint density function for the continuous variables does not exist. Conditional linear Gaussian distributions can handle ... -
No Double Counting Semantics for Conditional Independence
(Society for Imprecise Probability Theory and Applications (SIPTA, www.sipta.org), 2005-07)The main goal of this paper is to describe a new semantic for conditional independence in terms of no double counting of uncertain evidence. For ease of exposition, we use probability calculus to state all results. But the ... -
Nonlinear Deterministic Relationships in Bayesian Networks
(Springer-Verlag, 2005-07)In a Bayesian network with continuous variables containing a variable(s) that is a conditionally deterministic function of its continuous parents, the joint density function does not exist. Conditional linear Gaussian ... -
Inference in Hybrid Bayesian Networks with Mixtures of Truncated Exponentials
(University of Kansas School of Business, 2005-06)Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated with an MTE potential, which can ... -
Using Dempster-Shafer’s Belief-Function Theory in Expert Systems
(John Wiley & Sons, 1994-02)The main objective of this paper is to describe how Dempster-Shafer’s (DS) theory of belief functions fits in the framework of valuation-based systems (VBS). Since VBS serve as a framework for managing un-certainty in ... -
Conditional Independence in Valuation-Based Systems
(Elsevier Science Publishers B. V., 1994-04)This paper introduces the concept of conditional independence in valuation-based systems (VBS). VBS is an axiomatic framework capable of representing many different uncertainty calculi. We define conditional independence ... -
Using Possibility Theory in Expert Systems
(Elsevier Science Publishers B. V., 1992-12-10)This paper has two main objectives. The first objective is to give a characterization of a qualitative description of a possibility function. A qualitative description of a possibility function is called a consistent ... -
A Comparison of Graphical Techniques for Decision Analysis
(European Journal of Operational Research, 1994-10-13)Recently, we proposed a new method for representing and solving decision problems based on the framework of valuation-based systems. The new representation is called a valuation network, and the new solution method is ... -
Valuation-Based Systems for Bayesian Decision Analysis
(Operations Research Society of America, 1992-05)This paper proposes a new method for representing and solving Bayesian decision problems. The representation is called a valuation-based system and has some similari¬ties to influence diagrams. However, unlike influence ... -
Axioms for Dynamic Programming
(John Wiley & Sons Ltd, 1996)This paper describes an abstract framework, called valuation networks (VN), for representing and solving discrete optimization problems. In VNs, we represent information in an optimization problem using functions called ... -
A New Pruning Method for Solving Decision Trees and Game Trees
(Morgan Kaufmann Publishers, 1995-08)The main goal of this paper is to describe a newpruning method for solving decision trees and game trees. The pruning method for decision trees suggests a slight variant of decision trees that we call scenario trees. In ... -
Binary Join Trees
(Morgan Kaufmann Publishers, 1996)The main goal of this paper is to describe a datastructure called binary join trees that are useful incomputing multiple marginals efficiently usingthe Shenoy-Shafer architecture. We define binaryjoin trees, describe their ...
