AN APPROXIMATE METHOD FOR SAMPLING CORRELATED RANDOM-VARIABLES FROM PARTIALLY-SPECIFIED DISTRIBUTIONS

This paper presents an algorithm for generating correlated vectors of random numbers. The user need not fully specify the joint distribution function; instead, the user ''partially specifies'' only the marginal distributions and the correlation matrix. The algorithm may be applie d to any set of continuous, strictly increasing distribution functions ; the marginal distributions need not all be of the same functional fo rm. The correlation matrix is first checked for mathematical consisten cy (positive semi-definiteness), and adjusted if necessary. Then the c orrelated random vectors are generated using a combination of Cholesky decomposition and Gauss-Newton iteration. Applications are made to co st analysis, where correlations are often present between cost element s in a work breakdown structure.