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Stochastic Simulation and Monte Carlo Methods (Record no. 24016)

000 -LEADER
fixed length control field 04155nam a22004695i 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140310151459.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 130716s2013 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783642393631
978-3-642-39363-1
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA273.A1-274.9
Classification number QA274-274.9
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.2
Edition number 23
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg :
-- Imprint: Springer,
-- 2013.
912 ## -
-- ZDB-2-SMA
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Graham, Carl.
Relator term author.
245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE
Title Stochastic Simulation and Monte Carlo Methods
Medium [electronic resource] :
Remainder of title Mathematical Foundations of Stochastic Simulation /
Statement of responsibility, etc by Carl Graham, Denis Talay.
300 ## - PHYSICAL DESCRIPTION
Extent XVI, 260 p. 4 illus.
Other physical details online resource.
440 1# - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Stochastic Modelling and Applied Probability,
International Standard Serial Number 0172-4568 ;
Volume number/sequential designation 68
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Part I:Principles of Monte Carlo Methods -- 1.Introduction -- 2.Strong Law of Large Numbers and Monte Carlo Methods -- 3.Non Asymptotic Error Estimates for Monte Carlo Methods -- Part II:Exact and Approximate Simulation of Markov Processes -- 4.Poisson Processes -- 5.Discrete-Space Markov Processes -- 6.Continuous-Space Markov Processes with Jumps -- 7.Discretization of Stochastic Differential Equations -- Part III:Variance Reduction, Girsanov’s Theorem, and Stochastic Algorithms -- 8.Variance Reduction and Stochastic Differential Equations -- 9.Stochastic Algorithms -- References -- Index.
520 ## - SUMMARY, ETC.
Summary, etc 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 Itô 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.  
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematics.
Topical term or geographic name as entry element Finance.
Topical term or geographic name as entry element Numerical analysis.
Topical term or geographic name as entry element Distribution (Probability theory).
Topical term or geographic name as entry element Mathematics.
Topical term or geographic name as entry element Probability Theory and Stochastic Processes.
Topical term or geographic name as entry element Numerical Analysis.
Topical term or geographic name as entry element Quantitative Finance.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Talay, Denis.
Relator term author.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783642393624
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-642-39363-1
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type E-Book
Copies
Price effective from Permanent location Date last seen Not for loan Date acquired Source of classification or shelving scheme Koha item type Damaged status Lost status Withdrawn status Current location Full call number
2014-04-10AUM Main Library2014-04-10 2014-04-10 E-Book   AUM Main Library519.2
Languages: 
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