On numerical calculation of probabilities according to Dirichlet distribution

Ashraf Gouda and Tamás Szántai ()

Annals of Operations Research, 2010, vol. 177, issue 1, 185-200

Abstract: The main difficulty in numerical solution of probabilistic constrained stochastic programming problems is the calculation of the probability values according to the underlying multivariate probability distribution. In addition, when we are using a nonlinear programming algorithm for the solution of the problem, the calculation of the first and second order partial derivatives may also be necessary. In this paper we give solutions to the above problems in the case of Dirichlet distribution. For the calculation of the cumulative distribution function values, the Lauricella function series expansions will be applied up to 7 dimensions. For higher dimensions we propose the hypermultitree bound calculations and a variance reduction simulation procedure based on these bounds. There will be given formulae for the calculation of the first and second order partial derivatives, too. The common property of these formulae is that they involve only lower dimensional cumulative distribution function calculations. Numerical test results will also be presented. Copyright Springer Science+Business Media, LLC 2010

Keywords: Dirichlet distribution; Lauricella function; Probability bounds; Monte Carlo simulation; Variance reduction (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/s10479-009-0601-9

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