Stochastic Simulation: Algorithms and Analysis [electronic resource] / by Søren Asmussen, Peter W. Glynn.

By: Asmussen, Søren [author.]
Contributor(s): Glynn, Peter W [author.] | SpringerLink (Online service)
Material type: TextTextSeries: Stochastic Modelling and Applied Probability: 57Publisher: New York, NY : Springer New York, 2007Description: XIV, 476 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780387690339Subject(s): Mathematics | Operations research | Decision making | Economics, Mathematical | Management science | Probabilities | Statistics | Industrial engineering | Production engineering | Mathematics | Probability Theory and Stochastic Processes | Statistical Theory and Methods | Operation Research/Decision Theory | Industrial and Production Engineering | Operations Research, Management Science | Quantitative FinanceAdditional physical formats: Printed edition:: No titleDDC classification: 519.2 LOC classification: QA273.A1-274.9QA274-274.9Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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. The first half of the book focuses on general methods, whereas the second half discusses model-specific algorithms. 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. Søren Asmussen is a professor of Applied Probability at Aarhus University, Denmark and Peter Glynn is the Thomas Ford professor of Engineering at Stanford University.
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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.

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. The first half of the book focuses on general methods, whereas the second half discusses model-specific algorithms. 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. Søren Asmussen is a professor of Applied Probability at Aarhus University, Denmark and Peter Glynn is the Thomas Ford professor of Engineering at Stanford University.

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