000			04621nam a22003735i 4500
001			279935
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007			cr nn 008mamaa
008			150903s2007 xxu| o |||| 0|eng d
020			_a9780387714356 _99780387714356
024	7		_a10.1007/9780387714356 _2doi
035			_avtls000332148
039		9	_a201509030217 _bVLOAD _c201404122037 _dVLOAD _c201404091807 _dVLOAD _y201402041021 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aCastillo, Enrique Del. _eautor _9304539
245	1	0	_aProcess Optimization : _bA Statistical Approach / _cby Enrique Del Castillo.
264		1	_aBoston, MA : _bSpringer US, _c2007.
300			_brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aInternational Series in Operations Research & Management Science, _x0884-8289 ; _v105
500			_aSpringer eBooks
505	0		_aPreliminaries -- An Overview of Empirical Process Optimization -- Elements of Response Surface Methods -- Optimization Of First Order Models -- Experimental Designs For First Order Models -- Analysis and Optimization of Second Order Models -- Experimental Designs for Second Order Models -- Statistical Inference in Process Optimization -- Statistical Inference in First Order RSM Optimization -- Statistical Inference in Second Order RSM Optimization -- Bias Vs. Variance -- Robust Parameter Design and Robust Optimization -- Robust Parameter Design -- Robust Optimization -- Bayesian Approaches in Process Optimization -- to Bayesian Inference -- Bayesian Methods for Process Optimization -- to Optimization of Simulation and Computer Models -- Simulation Optimization -- Kriging and Computer Experiments -- Appendices -- Basics of Linear Regression -- Analysis of Variance -- Matrix Algebra and Optimization Results -- Some Probability Results Used in Bayesian Inference.
520			_aPROCESS OPTIMIZATION: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other "noisy" systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor/electronics manufacturing and in biotech manufacturing industries. The major features of PROCESS OPTIMIZATION: A Statistical Approach are: It provides a complete exposition of mainstream experimental design techniques, including designs for first and second order models, response surface and optimal designs; Discusses mainstream response surface method in detail, including unconstrained and constrained (i.e., ridge analysis and dual and multiple response) approaches; Includes an extensive discussion of Robust Parameter Design (RPD) problems, including experimental design issues such as Split Plot designs and recent optimization approaches used for RPD; Presents a detailed treatment of Bayesian Optimization approaches based on experimental data (including an introduction to Bayesian inference), including single and multiple response optimization and model robust optimization; Provides an in-depth presentation of the statistical issues that arise in optimization problems, including confidence regions on the optimal settings of a process, stopping rules in experimental optimization and more; Contains a discussion on robust optimization methods as used in mathematical programming and their application in response surface optimization; Offers software programs written in MATLAB and MAPLE to implement Bayesian and frequentist process optimization methods; Provides an introduction to the optimization of computer and simulation experiments including and introduction to stochastic approximation and stochastic perturbation stochastic approximation (SPSA) methods; Includes an introduction to Kriging methods and experimental design for computer experiments; Provides extensive appendices on Linear Regression, ANOVA, and Optimization Results.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387714349
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-71435-6 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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