Abstract
A Logistic-Normal random variable (Y) is obtained from a Normal random variable (X)
by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may
be used to model the branching probabilities. In some cases, the probabilities to
be modeled may not be independent, and a method for generating correlated Logistic-Normal
random variates would be useful. A technique for generating correlated Normal random
variates has been previously described. Using Taylor Series approximations and the
algebraic definitions of variance and covariance, we describe methods for estimating
the means, variances, and covariances of Normal random variates which, after translation
using the above formula, will result in Logistic-Normal random variates having approximately
the desired means, variances, and covariances. Multiple simulations of the method
using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.
Keywords
Decision Tree - Probabilistic - Monte Carlo - Correlation - LogisticNormal