John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
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. © 1972, ACM. All rights reserved.
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
S. Winograd
Journal of the ACM
Hiroshi Kanayama, Tetsuya Nasukawa
Natural Language Engineering
Yixin Diao, Kevin M. Passino
IEEE Transactions on Fuzzy Systems