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Wiley Serie in Wahrscheinlichkeit und Statistik Ser.: Numerische Methoden für...-
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eBay-Artikelnr.:134973937276
Artikelmerkmale
- Artikelzustand
- Subject
- Probability & Statistics / Stochastic Processes, Probability & Statistics / General
- ISBN
- 9780471546412
- Subject Area
- Mathematics
- Publication Name
- Numerical Methods for Stochastic Processes
- Publisher
- Wiley & Sons, Incorporated, John
- Item Length
- 9.5 in
- Publication Year
- 1994
- Series
- Wiley Series in Probability and Statistics Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.1 in
- Item Weight
- 24.1 Oz
- Item Width
- 6.3 in
- Number of Pages
- 384 Pages
Über dieses Produkt
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0471546410
ISBN-13
9780471546412
eBay Product ID (ePID)
342306
Product Key Features
Number of Pages
384 Pages
Language
English
Publication Name
Numerical Methods for Stochastic Processes
Subject
Probability & Statistics / Stochastic Processes, Probability & Statistics / General
Publication Year
1994
Type
Textbook
Subject Area
Mathematics
Series
Wiley Series in Probability and Statistics Ser.
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
24.1 Oz
Item Length
9.5 in
Item Width
6.3 in
Additional Product Features
Edition Number
1
Intended Audience
Scholarly & Professional
LCCN
93-010302
Dewey Edition
20
Series Volume Number
273
Illustrated
Yes
Dewey Decimal
519.2
Table Of Content
Preliminaries. Computation of Expectations in Finite Dimension. Simulation of Random Processes. Deterministic Resolution of Some Markovian Problems. Stochastic Differential Equations and Brownian Functionals. Notes. References. Index.
Synopsis
Gives greater rigor to numerical treatments of stochastic models. Contains Monte Carlo and quasi-Monte Carlo techniques, simulation of major stochastic procedures, deterministic methods adapted to Markovian problems and special problems related to stochastic integral and differential equations. Simulation methods are given throughout the text as well as numerous exercises., Gives greater rigor to numerical treatments of stochastic models. Contains Monte Carlo and quasi-Monte Carlo techniques, simulation of major stochastic procedures, deterministic methods adapted to Markovian problems and special problems related to stochastic integral and differential equations., In recent years, random variables and stochastic processes have emerged as important factors in predicting outcomes in virtually every field of applied and social science. Ironically, according to Nicolas Bouleau and Dominique Lepingle, the presence of randomness in the model sometimes leads engineers to accept crude mathematical treatments that produce inaccurate results. The purpose of Numerical Methods for Stochastic Processes is to add greater rigor to numerical treatment of stochastic processes so that they produce results that can be relied upon when making decisions and assessing risks. Based on a postgraduate course given by the authors at Paris 6 University, the text emphasizes simulation methods, which can now be implemented with specialized computer programs. Specifically presented are the Monte Carlo and shift methods, which use an "imitation of randomness" and have a wide range of applications, and the so-called quasi-Monte Carlo methods, which are rigorous but less widely applicable. Offering a broad introduction to the field, this book presents the current state of the main methods and ideas and the cases for which they have been proved. Nevertheless, the authors do explore problems raised by these newer methods and suggest areas in which further research is needed. Extensive notes and a full bibliography give interested readers the option of delving deeper into stochastic numerical analysis. For professional statisticians, engineers, and physical and social scientists, Numerical Methods for Stochastic Processes provides both the theoretical background and the necessary practical tools to improve predictions based on randomness in the model. With its exercises andbroad-spectrum coverage, it is also an excellent textbook for introductory graduate-level courses in stochastic process mathematics.
LC Classification Number
QA274.B67 1994
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