Titlebook: Stochastic Processes and Orthogonal Polynomials; Wim Schoutens Book 2000 Springer Science+Business Media New York 2000 Binomial distributi

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Stochastic Processes,endency, let . be a stochastic process defined on the parameter set .. We think of . in terms of time, and the values that .. can assume are called the . which are elements of a . ⊂ ℝ. A stochastic process is called a Markov process if it satisfies .for any value of t., t.> 0. To interpret (2.1), we

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Stein Approximation and Orthogonal Polynomials,imation error involved. The strengths of the method are that it can be applied in many circumstances in which dependence plays a part. A key tool in Stein’s theory is the generator method developed by Barbour [10]. In this chapter we show how orthogonal polynomials appear in this context in a natura

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monstrates how some real-world problems arising in engineering, economics, and other domains can be formulated as optimization problems..978-3-031-57322-4978-3-031-57320-0Series ISSN 1860-949X Series E-ISSN 1860-9503

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Wim Schoutens. We explore the effects of reducing complexity by partitioning the data domain of the optimization problem for a sequential approach that integrates energy models for constraint handling directly into the optimization process. We explore the effects of different partitioning schemes and evaluate th