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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Roles of the SCM Steering Departmention 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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Computational Probability978-0-387-74676-0Series ISSN 0884-8289 Series E-ISSN 2214-7934

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Dheeraj Kumar,Ravi Kant Singh,Apurba Layekause 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.

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https://doi.org/10.1007/978-3-8349-3948-7discrete 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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Overcoming Performance Trade-Offstational 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.

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John H. Drew,Diane L. Evans,Lawrence M. LeemisThis is an expository monograph with a downloadable modeling language, APPL, that will be used across the Applied Sciences domains including OR/MS, Applied Probability, Engineering, Statistics, Econom

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Computational Probabilityt to solve by hand, but are solvable with computational probability using A Probability Programming Language (APPL). We define the field of . as the development of data structures and algorithms to automate the derivation of existing and new results in probability and statistics. Section 10.3, for e

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Maple for APPLessions, plotting, and programming, just to name a few of the basics. APPL is, simply, a set of supplementary Maple commands and procedures that augments the existing computer algebra system. In effect, APPL takes the capabilities of Maple and turns it into a computer algebra system for computationa