Moment methods for decision analysis
Management Science
Correlations and Copulas for Decision and Risk Analysis
Management Science
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
On rank correlation measures for non-continuous random variables
Journal of Multivariate Analysis
Optimal Sequential Exploration: A Binary Learning Model
Decision Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Copulas-Based Approach to Modeling Dependence in Decision Trees
Operations Research
Approximating Joint Probability Distributions Given Partial Information
Decision Analysis
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The construction of a probabilistic model is a key step in most decision and risk analyses. Typically this is done by defining a single joint distribution in terms of marginal and conditional distributions. The difficulty of this approach is that often the joint distribution is underspecified. For example, we may lack knowledge of the marginal distributions or the underlying dependence structure. In this paper, we suggest an approach to analyzing decisions with partial information. Specifically, we propose a simulation procedure to create a collection of joint distributions that match the known information. This collection of distributions can then be used to analyze the decision problem. We demonstrate our method by applying it to the Eagle Airlines case study used in previous studies.