Kevin D. Reilly
James J. Buckley
Xidong Zheng
Francisco Hernandez
Monte Carlo, Statistics and Fuzzy Uncertain Probabilities
Proc. Huntsville Simulation Conference, 2002
Abstract
The senior author's earliest exposure to 'Monte Carlo and statistics' was
tutorial: how to develop and theorize about elementary statistics from a
consummate computational approach. It included developing through computation,
sampling distributions of the mean, (sample) variance, Student's t, Chi-Squared,
and Pearson's correlation coefficient. In follow-up studies, computations were
extended to newer frameworks, e.g., the "numerical recipes," with additional
experiments with Fisher's F in general linear model studies. Since some work of
this kind is designed to act as a "co- processor" to other computations, e.g.,
stochastic simulations, our most recent work involves simulation languages, the
choice, in this paper being Wolverine, Inc.'s relatively new SLX (Simulation
Language with eXtensibility), a system which includes a subset of GPSS couched
in a general milieu of object-directed computing. A brief overview of relevant
features is presented before we conclude with a departure relating to the
potentially extensive opportunity presented by the newly developing fuzzy
uncertain probability theory. Here, we plan to parallel conventional Monte
Carlo approaches to the maximum extent possible. Software engineering and forms
of "intelligent processing" (AI) features arose over the course of studies,
e.g., involving logic programming, expert guidance of choices, e.g., statistical
test choices and their configurations.
Key Words:
Monte Carlo techniques, standard statistics, Fuzzy probability and statistics,
simulation programming languages, combined (integrated) simulation styles