Advanced input modeling for simulation experimentation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Generating "dependent" quasi-random numbers
Proceedings of the 32nd conference on Winter simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Behavior of the NORTA method for correlated random vector generation as the dimension increases
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A theoretical basis for perturbation methods
Statistics and Computing
Advanced input modeling: properties of the NORTA method in higher dimensions
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
A Generalization of the Inventory Pooling Effect to Nonnormal Dependent Demand
Manufacturing & Service Operations Management
Risky Choices and Correlated Background Risk
Management Science
Data ShufflingA New Masking Approach for Numerical Data
Management Science
Decision Analysis
Optimal Sequential Exploration: A Binary Learning Model
Decision Analysis
Data quality from crowdsourcing: a study of annotation selection criteria
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
Multiattribute Utility Copulas
Operations Research
Does high forecast uncertainty preclude effective decision support?
Environmental Modelling & Software
Perturbation of Numerical Confidential Data via Skew-t Distributions
Management Science
Generalized Diagonal Band Copulas with Two-Sided Generating Densities
Decision Analysis
Assessing Joint Distributions with Isoprobability Contours
Management Science
A Copulas-Based Approach to Modeling Dependence in Decision Trees
Operations Research
Robust Simulation of Global Warming Policies Using the DICE Model
Management Science
A Simulation-Based Approach to Decision Making with Partial Information
Decision Analysis
Approximating Joint Probability Distributions Given Partial Information
Decision Analysis
CODA: high dimensional copula discriminant analysis
The Journal of Machine Learning Research
Cyber-risk decision models: To insure IT or not?
Decision Support Systems
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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 joint distribution in terms of marginal and conditional distributions for the model's random variables. We describe an alternative approach that uses a copula to construct joint distributions and pairwise correlations to incorporate dependence among the variables. The approach is designed specifically to permit the use of an expert's subjective judgments of marginal distributions and correlations. The copula that underlies the multivariate normal distribution provides the basis for modeling dependence, but arbitrary marginals are allowed. We discuss how correlations can be assessed using techniques that are familiar to decision analysts, and we report the results of an empirical study of the accuracy of the assessment methods. The approach is demonstrated in the context of a simple example, including a study of the sensitivity of the results to the assessed correlations.