Showing papers on "Probability-generating function published in 1990"

A Random Sampling Discrete Probability Algorithm with Condensation for Probabilistic Analysis

Abstract: Discrete probability methods have several advantages that should be retained in constructing a probabilistic model. First, most engineering data are in a discrete form, and thus a discrete probability method is a natural choice for incorporating such data in an analysis. Second, the discrete probability methods are invariant; i.e., regardless of the weighting scheme used for the input variable distributions, no new coding is required to implement these schemes. Other weighting methods, for example, Monte Carlo importance sampling, can require significant re-coding before lowprobability results can be estimated. The most significant drawback to discrete probability methods is that their application is limited. These discrete methods require many calculations and a large amount of computer storage space. The number of storage spaces equals the number of discrete points ND raised to the power of the number of variables Nv. Thus, for ten discrete and nine input variables, the response variable is char...