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/

  • Authors:
    • Bayes, A J
  • Publication Date: 1972-10

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Filing Info

  • Accession Number: 00227375
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 8 1973 12:00AM