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Beschreibung
Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. Given the wide range of examples, exercises and applications students, practitioners and researchers in probability, statistics, operations research, economics, finance, engineering as well as biology and chemistry and physics will find the book of value.
Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. Given the wide range of examples, exercises and applications students, practitioners and researchers in probability, statistics, operations research, economics, finance, engineering as well as biology and chemistry and physics will find the book of value.
Zusammenfassung
Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. It is the first rigorous and comprehensive advanced book on stochastic simulation. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focuses on general methods; the second half discusses model-specific algorithms. A large amount of exercises and illustrations are included, making the book of value to students, practitioners and researchers in a broad range of fields.
Inhaltsverzeichnis
General Methods and Algorithms.- Generating Random Objects.- Output Analysis.- Steady-State Simulation.- Variance-Reduction Methods.- Rare-Event Simulation.- Derivative Estimation.- Stochastic Optimization.- Algorithms for Special Models.- Numerical Integration.- Stochastic Di3erential Equations.- Gaussian Processes.- Lèvy Processes.- Markov Chain Monte Carlo Methods.- Selected Topics and Extended Examples.- What This Book Is About.- What This Book Is About.
Details
Erscheinungsjahr: | 2010 |
---|---|
Fachbereich: | Wahrscheinlichkeitstheorie |
Genre: | Mathematik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Reihe: | Stochastic Modelling and Applied Probability |
Inhalt: |
xiv
476 S. 44 s/w Illustr. 1 farbige Illustr. |
ISBN-13: | 9781441921468 |
ISBN-10: | 144192146X |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: |
Glynn, Peter W.
Asmussen, Søren |
Auflage: | Softcover reprint of hardcover 1st ed. 2007 |
Hersteller: |
Springer New York
Springer US, New York, N.Y. Stochastic Modelling and Applied Probability |
Maße: | 235 x 155 x 27 mm |
Von/Mit: | Peter W. Glynn (u. a.) |
Erscheinungsdatum: | 19.11.2010 |
Gewicht: | 0,739 kg |
Zusammenfassung
Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. It is the first rigorous and comprehensive advanced book on stochastic simulation. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focuses on general methods; the second half discusses model-specific algorithms. A large amount of exercises and illustrations are included, making the book of value to students, practitioners and researchers in a broad range of fields.
Inhaltsverzeichnis
General Methods and Algorithms.- Generating Random Objects.- Output Analysis.- Steady-State Simulation.- Variance-Reduction Methods.- Rare-Event Simulation.- Derivative Estimation.- Stochastic Optimization.- Algorithms for Special Models.- Numerical Integration.- Stochastic Di3erential Equations.- Gaussian Processes.- Lèvy Processes.- Markov Chain Monte Carlo Methods.- Selected Topics and Extended Examples.- What This Book Is About.- What This Book Is About.
Details
Erscheinungsjahr: | 2010 |
---|---|
Fachbereich: | Wahrscheinlichkeitstheorie |
Genre: | Mathematik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Reihe: | Stochastic Modelling and Applied Probability |
Inhalt: |
xiv
476 S. 44 s/w Illustr. 1 farbige Illustr. |
ISBN-13: | 9781441921468 |
ISBN-10: | 144192146X |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: |
Glynn, Peter W.
Asmussen, Søren |
Auflage: | Softcover reprint of hardcover 1st ed. 2007 |
Hersteller: |
Springer New York
Springer US, New York, N.Y. Stochastic Modelling and Applied Probability |
Maße: | 235 x 155 x 27 mm |
Von/Mit: | Peter W. Glynn (u. a.) |
Erscheinungsdatum: | 19.11.2010 |
Gewicht: | 0,739 kg |
Warnhinweis