A Minimum Variance Sampling Technique for Simulation Models

IN A NORMAL SIMULATION RUN, THE STATES OF THE MODEL ARE SAMPLED IN PROPORTION TO THEIR NATURAL FREQUENCY OF OCCURRENCE. FOR A GIVEN SAMPLING EFFORT, THIS DOES NOT IN GENERAL ESTIMATE A GIVEN STATISTIC OF THE MODEL WITH MAXIMUM PRECISION. A SAMPLING THEORY OF MARKOV CHAINS IS DEVELOPED WHICH ALLOWS SOME STATISTICS OF THE MARKOV STATE FREQUENCIES TO BE ESTIMATED WITH MINIMUM VARIANCE FOR A GIVEN SAMPLING EFFORT. A TECHNIQUE IS PRESENTED TO ALLOW THE SAMPLING FREQUENCY OF THE STATES OF THE SIMULATION TO BE INDEPENDENT OF THEIR NATURAL FREQUENCY. BY REPRESENTING A SIMULATION MODEL AS A MARKOV CHAIN, THE THEORY IS APPLIED TO ESTIMATE SOME STATISTICS OF THE SIMULATION MODEL WITH MINIMUM VARIANCE; FOR INSTANCE, THE FREQUENCY OF OVERLOAD OF A TELEPROCESSING COMPUTER SYSTEM. A NUMERICAL CASE IS PRESENTED IN WHICH THE SAMPLING EFFORT IS REDUCED BY A FACTOR OF SIXTY COMPARED TO A NORMAL SIMULATION RUN. /AUTHOR/