Analytical Methods for Dynamic Modelers

Analytical Methods for Dynamic Modelers
Author :
Publisher : MIT Press
Total Pages : 443
Release :
ISBN-10 : 9780262331432
ISBN-13 : 0262331438
Rating : 4/5 (438 Downloads)

Book Synopsis Analytical Methods for Dynamic Modelers by : Hazhir Rahmandad

Download or read book Analytical Methods for Dynamic Modelers written by Hazhir Rahmandad and published by MIT Press. This book was released on 2015-11-27 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: A user-friendly introduction to some of the most useful analytical tools for model building, estimation, and analysis, presenting key methods and examples. Simulation 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. Contributors Wenyi 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, Gönenç Yücel


Analytical Methods for Dynamic Modelers Related Books

Analytical Methods for Dynamic Modelers
Language: en
Pages: 443
Authors: Hazhir Rahmandad
Categories: Business & Economics
Type: BOOK - Published: 2015-11-27 - Publisher: MIT Press

DOWNLOAD EBOOK

A user-friendly introduction to some of the most useful analytical tools for model building, estimation, and analysis, presenting key methods and examples. Simu
Analytical System Dynamics
Language: en
Pages: 335
Authors: Brian Fabien
Categories: Technology & Engineering
Type: BOOK - Published: 2008-11-09 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

"Analytical System Dynamics: Modeling and Simulation" combines results from analytical mechanics and system dynamics to develop an approach to modeling constrai
Statistical Methods for Modeling Human Dynamics
Language: en
Pages: 442
Authors: Sy-Miin Chow
Categories: Psychology
Type: BOOK - Published: 2011-02-25 - Publisher: Routledge

DOWNLOAD EBOOK

This interdisciplinary volume features contributions from researchers in the fields of psychology, neuroscience, statistics, computer science, and physics. Stat
System Dynamics Modeling with R
Language: en
Pages: 188
Authors: Jim Duggan
Categories: Computers
Type: BOOK - Published: 2016-06-14 - Publisher: Springer

DOWNLOAD EBOOK

This new interdisciplinary work presents system dynamics as a powerful approach to enable analysts build simulation models of social systems, with a view toward
Dynamic Models in Biology
Language: en
Pages: 352
Authors: Stephen P. Ellner
Categories: Science
Type: BOOK - Published: 2011-09-19 - Publisher: Princeton University Press

DOWNLOAD EBOOK

From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universitie