000			03495nam a2200481 i 4500
001			7347042
003			IEEE
005			20190220121652.0
006			m o d
007			cr |n|||||||||
008			151229s2015 mau ob 001 eng d
010			_z 2015009374 (print)
020			_a9780262331449 _qelectronic
020			_z9780262029490 _qhardcover : print
035			_a(CaBNVSL)mat07347042
035			_a(IDAMS)0b00006484b080cb
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aT57.62 _b.A466 2015eb
082	0	0	_a511/.8 _223
245	0	0	_aAnalytical methods for dynamics modelers / _cedited by Hazhir Rahmandad, Rogelio Oliva, and Nathaniel D. Osgood.
264		1	_aCambridge, Massachusetts : _bMIT Press, _c[2015]
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2015]
300			_a1 PDF (448 pages).
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
504			_aIncludes bibliographical references and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aSimulation modeling is increasingly integrated into research and policy analysis of complex sociotechnical systems in a variety of domains. Model-based analysis and policy design inform a range of applications in fields from economics to engineering to health care. This book offers a hands-on introduction to key analytical methods for dynamic modeling. Bringing together tools and methodologies from fields as diverse as computational statistics, econometrics, and operations research in a single text, the book can be used for graduate-level courses and as a reference for dynamic modelers who want to expand their methodological toolbox.The focus is on quantitative techniques for use by dynamic modelers during model construction and analysis, and the material presented is accessible to readers with a background in college-level calculus and statistics. Each chapter describes a key method, presenting an introduction that emphasizes the basic intuition behind each method, tutorial style examples, references to key literature, and exercises. The chapter authors are all experts in the tools and methods they present. The book covers estimation of model parameters using quantitative data; understanding the links between model structure and its behavior; and decision support and optimization. An online appendix offers computer code for applications, models, and solutions to exercises.ContributorsWenyi An, Edward G. Anderson Jr., Yaman Barlas, Nishesh Chalise, Robert Eberlein, Hamed Ghoddusi, Winfried Grassmann, Peter S. Hovmand, Mohammad S. Jalali, Nitin Joglekar, David Keith, Juxin Liu, Erling Moxnes, Rogelio Oliva, Nathaniel D. Osgood, Hazhir Rahmandad, Raymond Spiteri, John Sterman, Jeroen Struben, Burcu Tan, Karen Yee, Gonen Ycel.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/29/2015.
650		0	_aSimulation methods.
650		0	_aSystem analysis.
655		0	_aElectronic books.
700	1		_aRahmandad, Hazhir.
700	1		_aOliva, Rogelio.
700	1		_aOsgood, Nathaniel D., _d1968-
710	2		_aIEEE Xplore (Online Service), _edistributor.
710	2		_aMIT Press, _epublisher.
776	0	8	_iPrint version: _z9780262029490
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=7347042
999			_c39702 _d39702