Titlebook: Computational Probability; Algorithms and Appli John H. Drew,Diane L. Evans,Lawrence M. Leemis Book 20081st edition Springer-Verlag US 2008

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Data Structures and Simple Algorithmsause they are defined with a somewhat simpler data structure than that for discrete random variables. The development described here gives a probabilist the ability to automate the instantiation and processing of continuous random variables—key elements of computational probability.

exhilaration

indoctrinate

Products of Random Variablesmented in the Product procedure in APPL. The algorithm behind the Transform procedure from the previous chapter differs fundamentally from the algorithm behind the Product procedure in that the former concerns the transformation of just . random variable and the latter concerns the product of . rand

擁護(hù)

Data Structures and Simple Algorithmsdiscrete random variables. The first section will show that the nature of the support of discrete random variables makes the data structures required much more complicated than for continuous random variables.

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Stochastic Simulationtational probability in input modeling. Section 10.3 contains a development of an algorithm to find the distribution of the Kolmogorov—Smirnov goodness of-fit test statistic in the all-parameters-known case.

蛙鳴聲

Other Applicationsion of the longest path of a series—parallel stochastic activity network with continuous activity durations. Section 11.2 concerns the use of APPL in determining whether a continuous random variable obeys Benford’s law. Finally, Section 11.3 contains miscellaneous computational probability applicati

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保留

filicide

Preeti,Supriyo Roy,Kaushik Kumarhm behind the Product procedure in that the former concerns the transformation of just . random variable and the latter concerns the product of . random variables. Some examples demonstrate the algorithm’s application.

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