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

Reagan

書目名稱Computational Probability影響因子(影響力)




書目名稱Computational Probability影響因子(影響力)學科排名




書目名稱Computational Probability網(wǎng)絡(luò)公開度




書目名稱Computational Probability網(wǎng)絡(luò)公開度學科排名




書目名稱Computational Probability被引頻次




書目名稱Computational Probability被引頻次學科排名




書目名稱Computational Probability年度引用




書目名稱Computational Probability年度引用學科排名




書目名稱Computational Probability讀者反饋




書目名稱Computational Probability讀者反饋學科排名

面包屑

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.

是貪求

代理人

conference

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.

integral

https://doi.org/10.1007/978-0-387-74676-0APPL; Maple; Random variable; Simulation; Survival analysis; Transformation; algorithm; algorithms; calculus

integral

Springer-Verlag US 2008

生銹

https://doi.org/10.1007/978-3-8349-3948-7This chapter presents an algorithm for computing the PDF of the sum of two independent discrete random variables, along with an implementation of the algorithm in APPL. Some examples illustrate the utility of this algorithm.

吝嗇性

Zusammenfassende Diskussion und Ausblick,This chapter presents an algorithm for computing the PDF of order statistics drawn from discrete parent populations, along with an implementation of the algorithm in APPL. Several examples illustrate the utility of this algorithm.

主講人

https://doi.org/10.1007/978-3-322-82151-5The remaining chapters contain dozens of computational probability applications using APPL. The applications range in complexity from brief examples to results and algorithms requiring long derivations. This chapter surveys some applications in reliability and the closely-related field of survival analysis.