Hardcover. Etat : Bon. Edition 2013. Ammareal reverse jusqu'� 15% du prix net de cet article � des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Edition 2013. Ammareal gives back up to 15% of this item's net price to charity organizations. N� de r�f. du vendeur F-328-324

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Stochastic Simulation and Monte Carlo Methods : Mathematical Foundations of Stochastic Simulation

Denis Talay

Edit� par Springer Berlin Heidelberg, 2013

ISBN 10 : 3642393624 ISBN 13 : 9783642393624

Neuf Couverture rigide

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - In various scientific and industrial fields, stochastic simulations are taking on a new importance. This is due to the increasing power of computers and practitioners' aim to simulate more and more complex systems, and thus use random parameters as well as random noises to model the parametric uncertainties and the lack of knowledge on the physics of these systems. The error analysis of these computations is a highly complex mathematical undertaking. Approaching these issues, the authors present stochastic numerical methods and prove accurate convergence rate estimates in terms of their numerical parameters (number of simulations, time discretization steps). As a result, the book is a self-contained and rigorous study of the numerical methods within a theoretical framework. After briefly reviewing the basics, the authors first introduce fundamental notions in stochastic calculus and continuous-time martingale theory, then develop the analysis of pure-jump Markov processes, Poisson processes, and stochastic differential equations. In particular, they review the essential properties of It� integrals and prove fundamental results on the probabilistic analysis of parabolic partial differential equations. These results in turn provide the basis for developing stochastic numerical methods, both from an algorithmic and theoretical point of view. The book combines advanced mathematical tools, theoretical analysis of stochastic numerical methods, and practical issues at a high level, so as to provide optimal results on the accuracy of Monte Carlo simulations of stochastic processes. It is intended for master and Ph.D. students in the field of stochastic processes and their numerical applications, as well as for physicists, biologists, economists and other professionals working with stochastic simulations, who will benefit from the ability to reliably estimate and control the accuracy of their simulations. N� de r�f. du vendeur 9783642393624

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Stochastic Simulation and Monte Carlo Methods: Mathematical Foundations of Stochastic Simulation (Stochastic Modelling and Applied Probability, 68)

Graham, Carl; Talay, Denis

Edit� par Springer, 2013

ISBN 10 : 3642393624 ISBN 13 : 9783642393624

Neuf Couverture rigide

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Etat : New. N� de r�f. du vendeur ABLIING23Mar3113020225866

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Stochastic Simulation and Monte Carlo Methods: Mathematical Foundations of Stochastic Simulation (Stochastic Modelling and Applied Probability, 68)

Graham, Carl; Talay, Denis

Edit� par Springer, 2013

ISBN 10 : 3642393624 ISBN 13 : 9783642393624

Neuf Couverture rigide

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Stochastic Simulation and Monte Carlo Methods: Mathematical Foundations of Stochastic Simulation (Stochastic Modelling and Applied Probability, 68)

Graham, Carl; Talay, Denis

Edit� par Springer, 2013

ISBN 10 : 3642393624 ISBN 13 : 9783642393624

Neuf Couverture rigide

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Etat : New. In English. N� de r�f. du vendeur ria9783642393624_new

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Stochastic Simulation and Monte Carlo Methods

Denis Talay

Edit� par Springer Berlin Heidelberg Jul 2013, 2013

ISBN 10 : 3642393624 ISBN 13 : 9783642393624

Neuf Couverture rigide

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In various scientific and industrial fields, stochastic simulations are taking on a new importance. This is due to the increasing power of computers and practitioners' aim to simulate more and more complex systems, and thus use random parameters as well as random noises to model the parametric uncertainties and the lack of knowledge on the physics of these systems. The error analysis of these computations is a highly complex mathematical undertaking. Approaching these issues, the authors present stochastic numerical methods and prove accurate convergence rate estimates in terms of their numerical parameters (number of simulations, time discretization steps). As a result, the book is a self-contained and rigorous study of the numerical methods within a theoretical framework. After briefly reviewing the basics, the authors first introduce fundamental notions in stochastic calculus and continuous-time martingale theory, then develop the analysis of pure-jump Markov processes, Poisson processes, and stochastic differential equations. In particular, they review the essential properties of It� integrals and prove fundamental results on the probabilistic analysis of parabolic partial differential equations. These results in turn provide the basis for developing stochastic numerical methods, both from an algorithmic and theoretical point of view. The book combines advanced mathematical tools, theoretical analysis of stochastic numerical methods, and practical issues at a high level, so as to provide optimal results on the accuracy of Monte Carlo simulations of stochastic processes. It is intended for master and Ph.D. students in the field of stochastic processes and their numerical applications, as well as for physicists, biologists, economists and other professionals working with stochastic simulations, who will benefit from the ability to reliably estimate and control the accuracy of their simulations. 276 pp. Englisch. N� de r�f. du vendeur 9783642393624

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