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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Computational Probabilityevelopment of data structures and algorithms to automate the derivation of existing and new results in probability and statistics. Section 10.3, for example, contains the derivation of the distribution of a well-known test statistic that requires 99500 carefully crafted integrations.

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Products of Random Variableshm 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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Other Applicationsdetermining whether a continuous random variable obeys Benford’s law. Finally, Section 11.3 contains miscellaneous computational probability applications that are not covered elsewhere in the monograph.

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Dheeraj Kumar,Ravi Kant Singh,Apurba Layekf basic numeric computation, then advance to defining variables, symbolic computations, functions, data types, solving equations, calculus and graphing. Then we will discuss the programming features of Maple that facilitate building the APPL language: loops, conditions and procedures.

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