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標(biāo)題: Titlebook: Design and Analysis of Simulation Experiments; Jack P.C. Kleijnen Book 2015Latest edition Springer International Publishing Switzerland 20 [打印本頁]

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作者: optic-nerve    時間: 2025-3-21 21:37
Book 2015Latest editionential bifurcation for multiple types of simulation responses.? Whereas all references in the original edition were placed at the end of the book, in this edition references are placed at the end of each chapter..From Reviews of the First Edition:.“Jack Kleijnen has once again produced a cutting-edg
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Grammatical Concepts for Pedagogical Grammaretermination .. and the adjusted coefficient ., and cross-validation; this section also discusses how classic low-order polynomial metamodels (detailed in the preceding chapter) may be improved in practice. Section 3.7 summarizes the major conclusions of this chapter. The chapter ends with solutions
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Classic Regression Metamodels and Their Designs,d-degree polynomials, focussing on . (CCDs). Section?2.10 briefly examines “optimal” designs and other designs. Section?2.11 summarizes the major conclusions of this chapter. The chapter ends with appendixes, solutions for the exercises, and references.
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Screening the Many Inputs of Realistic Simulation Models,ined through Wald’s sequential probability ratio test (SPRT). Section?4.6 discusses . (MSB), which extends SB to problems with multiple types of simulation responses (multivariate output). Section?4.7 discusses validation of the SB and MSB assumptions. Section?4.8summarizes the major conclusions of
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Kriging Metamodels and Their Designs,ing models through . (FANOVA) using Sobol’s indexes. Section 5.9 discusses . (RA) or . (UA). Section 5.10 discusses several remaining issues. Section 5.11 summarizes the major conclusions of this chapter, and suggests topics for future research. The chapter ends with Solutions of exercises, and a lo
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Simulation Optimization,imated optimum is truly optimal—using the . (KKT) conditions. Section?6.3 discusses the use of Kriging metamodels for optimization. Section?6.3.1 presents . (EGO), which uses Kriging. Section?6.3.2 presents . (KrIMP) for the solution of problems with constrained outputs. Section?6.4 discusses . (RO)
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Book 2015Latest editiontogether, this new edition has approximately 50% new material not in the original book.? More specifically, the author has made significant changes to the book’s organization, including placing the chapter on Screening Designs immediately after the chapters on Classic Designs, and reversing the orde
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Classic Regression Metamodels and Their Designs,iscusses black-box versus white-box approaches in the . (DASE). Section?2.2 covers the basics of linear regression analysis. Section?2.3 focuses on first-order polynomial regression. Section?2.4 presents designs for estimating such first-order polynomials; namely, so-called . (R-III) designs. Sectio
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Classic Assumptions Versus Simulation Practice,. Section 3.2 discusses multiple simulation outputs (responses, performance measures), which are usual in simulation practice. Section 3.3 addresses possible nonnormality of either the simulation output itself or the regression residuals (fitting errors), including tests of normality, normalizing tr
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Screening the Many Inputs of Realistic Simulation Models,lation models that have “very many” inputs (say, hundreds of inputs); this section also gives an overview of several screening methods. Section?4.2 explains a screening method called . (SB); for simplicity, this section assumes deterministic simulation and first-order polynomial metamodels. Section?
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Kriging Metamodels and Their Designs,ion 5.2 details so-called . (OK), including the basic Kriging assumptions and formulas assuming deterministic simulation. Section 5.3 discusses parametric bootstrapping and conditional simulation for estimating the variance of the OK predictor. Section 5.4 discusses . (UK) in deterministic simulatio
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Simulation Optimization,simulation; this optimization we call . or briefly .. There are many methods for this optimization, but we focus on methods that use specific metamodels of the underlying simulation models; these metamodels were detailed in the preceding chapters, and use either linear regression or Kriging. Section
作者: 考古學(xué)    時間: 2025-3-25 01:49
Amber Dudley,Roumyana Slabakovaiscusses black-box versus white-box approaches in the . (DASE). Section?2.2 covers the basics of linear regression analysis. Section?2.3 focuses on first-order polynomial regression. Section?2.4 presents designs for estimating such first-order polynomials; namely, so-called . (R-III) designs. Sectio
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International Series in Operations Research & Management Sciencehttp://image.papertrans.cn/d/image/268514.jpg
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Design and Analysis of Simulation Experiments978-3-319-18087-8Series ISSN 0884-8289 Series E-ISSN 2214-7934
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https://doi.org/10.1007/978-3-319-18087-8Deterministic Simulation; Discrete-Event Simulation; Kriging; Metamodels; Polynomial Metamodels; Simulati
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Neue Aspekte der Wissenschaftstheorieng chapter, which does not claim to be exhaustive. Many research findings are based on studies with healthy subjects, and the same conclusions cannot necessarily be extrapolated for neurorehabilitation. As the principles of motor learning cannot be applied to all aspects of facial-oral functions, many questions remain open.
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Robert Chr. van Ooyen,Martin H. W. M?llersby the authors on managing your career that first appeared in Science‘s Next Wave..Academic Scientists at Work is a valuable resource for the career scientist who demands and expects the best..978-0-387-32176-9978-0-387-35427-9
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deployments. Affordability considerations and priority of services inspire innovative architectural and optimization solutions to the infrastructure, choice of applications and user terminals.978-1-4419-3884-8978-0-387-49064-9
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