Sample-based non-uniform random variate generation

Sample-based non-uniform random variate generation Proceedings of the 1086 Winter Simulation 3. Wilson, J. Henriksen, S. Roberts (eds.) Conference SAMPLEBASED NON-UNIFORM GENERATION RANDOM VARIATE ABSTRACT. A sample of n lid random variables wlth a given unknown density Is given. We discuss several Issues related to the problem of generatlng a new sample of Ild random varlables wlth almost the same denslty. In particular, we look at sample Independence, consistency, sample lndlstlngulshablllty, moment matching and generator efnclency. We also introduce the notlon of a replacement number, the mlnlmum number of obsenratlons In a glven sample that have to be replaced to obtaln a sample wlth a glven denslty. -P(YEA)P(XEB)I . where the supremum Is wlth respect to all Bore1 sets A of II ™ and all Bore1 sets B of R nd , and where Y = Y, and X 1s our shorthand notatlon for (X,, , X, ). We say that the samples are asymptotically Independent when llm D, = o . œ-+a3 Ln sltuatlons In which X,, , X,, ls used to deslgn or build a system, and Y,, . , Y,,, Is used to test It. the sample dependence often causes optlmlstlc evaluations. Wlthout the asymptotic Independence. we can ™t even http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Sample-based non-uniform random variate generation

Association for Computing Machinery — Dec 1, 1986

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Datasource
Association for Computing Machinery
Copyright
Copyright © 1986 by ACM Inc.
ISBN
0-911801-11-1
D.O.I.
10.1145/318242.318443
Publisher site
See Article on Publisher Site

Abstract

Proceedings of the 1086 Winter Simulation 3. Wilson, J. Henriksen, S. Roberts (eds.) Conference SAMPLEBASED NON-UNIFORM GENERATION RANDOM VARIATE ABSTRACT. A sample of n lid random variables wlth a given unknown density Is given. We discuss several Issues related to the problem of generatlng a new sample of Ild random varlables wlth almost the same denslty. In particular, we look at sample Independence, consistency, sample lndlstlngulshablllty, moment matching and generator efnclency. We also introduce the notlon of a replacement number, the mlnlmum number of obsenratlons In a glven sample that have to be replaced to obtaln a sample wlth a glven denslty. -P(YEA)P(XEB)I . where the supremum Is wlth respect to all Bore1 sets A of II ™ and all Bore1 sets B of R nd , and where Y = Y, and X 1s our shorthand notatlon for (X,, , X, ). We say that the samples are asymptotically Independent when llm D, = o . œ-+a3 Ln sltuatlons In which X,, , X,, ls used to deslgn or build a system, and Y,, . , Y,,, Is used to test It. the sample dependence often causes optlmlstlc evaluations. Wlthout the asymptotic Independence. we can ™t even

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