Titlebook: Computational Probability; Algorithms and Appli John H. Drew,Diane L. Evans,Lawrence M. Leemis Book 2017Latest edition Springer Internation

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书目名称Computational Probability影响因子(影响力)




书目名称Computational Probability影响因子(影响力)学科排名




书目名称Computational Probability网络公开度




书目名称Computational Probability网络公开度学科排名




书目名称Computational Probability被引频次




书目名称Computational Probability被引频次学科排名




书目名称Computational Probability年度引用




书目名称Computational Probability年度引用学科排名




书目名称Computational Probability读者反馈




书目名称Computational Probability读者反馈学科排名

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Book 2017Latest editionook examines and presents, in a systematic manner, computational probability methods that encompass data structures and algorithms. The developed techniques address problems that require exact probability calculations, many of which have been considered intractable in the past. The book addresses th

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Instrumente des Supply Chain Managements, the development of data structures and algorithms to automate the derivation of existing and new results in probability and statistics. Section 12.3, for example, contains the derivation of the distribution of a well-known test statistic that requires 99,500 carefully crafted integrations.

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Future of Supply Chain Managemental. [.]. The bivariate transformation procedure presented in this chapter handles 1-to-1, .-to-1, and piecewise .-to-1 transformations for both independent and dependent random variables. We also present other procedures that operate on bivariate random variables (e.g., calculating correlation and marginal distributions).

必死

Future of Supply Chain Management behind the . procedure in that the transformation algorithms are more general whereas determining the distribution of the product of two random variables is more specific. Some examples given in the chapter demonstrate the algorithm’s application.

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Strategien des Supply Chain Management,eveloped. Maple computer code is developed to implement the transient queue analysis for many system measures of performance without regard to traffic intensity (i.e., the system may be unstable with traffic intensity greater than one).

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Computational Probability the development of data structures and algorithms to automate the derivation of existing and new results in probability and statistics. Section 12.3, for example, contains the derivation of the distribution of a well-known test statistic that requires 99,500 carefully crafted integrations.

切割

Bivariate Transformations of Random Variablesal. [.]. The bivariate transformation procedure presented in this chapter handles 1-to-1, .-to-1, and piecewise .-to-1 transformations for both independent and dependent random variables. We also present other procedures that operate on bivariate random variables (e.g., calculating correlation and marginal distributions).