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

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https://doi.org/10.1007/978-3-319-43323-3APPL; Computational Probability; Continuous Random Variables; Discrete Random Variables; Maple; Multicrit

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https://doi.org/10.1007/978-3-8349-9549-0monious and flexible mechanism for modeling the evolution of a time series. Some useful measures of these models (e.g., the autocorrelation function or the spectral density function) are oftentimes tedious to compute by hand, and APPL can help ease the computational burden.

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Instrumente des Supply Chain Managements,leasant to solve by hand, but are solvable with computational probability using APPL (A Probability Programming Language). 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 12.3,

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Oliver Lawrenz,Michael Nenningery 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.