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A. J. Bayes

dblp:13/5744 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1972
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation
simulation		 0.011972
A Minimum Variance Sampling Technique for Simulation Models · J. ACM 1972
Performance modeling and evaluation › simulation
variance reduction		 0.011972
A Minimum Variance Sampling Technique for Simulation Models · J. ACM 1972

Methods — techniques the papers use, named apart from their topics

minimum variance sampling · 0.0markov chain theory · 0.0
YearPublicationVenuePosition
1972 A Minimum Variance Sampling Technique for Simulation Models
abstract
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.
A. J. Bayes
 J. ACM1