Discrete Approximations of Joint Probability Distributions
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Practical computational limits for stochastic decision analysis models often require that probability distributions have a modest number of points with positive mass. This paper develops an approach to constructing such discrete joint probability distributions which introduces less bias than more commonly used methods. The method, based on solving systems of nonlinear equations, is demonstrated for both continuous and discrete distributions.