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Numerical Solution of Stochastic Differential Equations (Stochastic Modelling and Applied Probability)

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Review

"... the authors draw upon their own research and experiences in obviously many disciplines... considerable time has obviously been spent writing this in the simplest language possible. This was not an easy task... Their exposition stresses clarity, not formality - a very welcome approach." ZAMP

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From the Back Cover

The numerical analysis of stochastic differential equations differs significantly from that of ordinary differential equations due to peculiarities of stochastic calculus. This book provides an introduction to stochastic calculus and stochastic differential equations, in both theory and applications, emphasising the numerical methods needed to solve such equations. It assumes of the reader an undergraduate background in mathematical methods typical of engineers and physicists, though many chapters begin with a descriptive summary. The book is also accessible to others who only require numerical recipes. The stochastic Taylor expansion provides the basis for the discrete time numerical methods for differential equations. The book presents many new results on high-order methods for strong sample path approximations and for weak functional approximations, including implicit, predictor-corrector, extra-polation and variance-reduction methods. Besides serving as a basic text on such methods, the book offers the reader ready access to a large number of potential research problems in a field that is just beginning to expand rapidly and is widely applicable. To help the reader to develop an intuitive understanding of the underlying mathematics and hand-on numerical skills, exercises and over 100 PC-Exercises are included.

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Product details

Series: Stochastic Modelling and Applied Probability (Book 23)

Hardcover: 636 pages

Publisher: Springer; Corrected edition (August 1992)

Language: English

ISBN-10: 9783540540625

ISBN-13: 978-3540540625

ASIN: 3540540628

Product Dimensions:

6.1 x 1.5 x 9.2 inches

Shipping Weight: 2.4 pounds (View shipping rates and policies)

Average Customer Review:

3.9 out of 5 stars

8 customer reviews

Amazon Best Sellers Rank:

#1,051,571 in Books (See Top 100 in Books)

This book covers a lot of stuff about simulation of SDEs. It includes descriptions of various higher-order techniques, something you won't find elsewhere but can be useful for programmers looking to tune their implementations. However, despite the vast amount of material in this book, it's pretty dry. The practicality of different methods isn't really discussed very well... why would I choose implicit over explicit? how high order is really worth it? what about efficiency of sampling the cross-term integrals? Somehow I also felt the theory did not go very far; nothing in this book was new to me, having read some intro stoch calc before.Generally I would not recommend this book, except as a reference for high-order simulation methods.

Great, great book. However, the font and typesetting is very outdated and somewhat hard on the eye. Sould be re-done with latex.

Great!

Good

Much literature is published on numerical methods for stochastic differential systems but most of it focuses on their use in pricing financial products. There is genuinely a lack of reference books that provide a stronger mathematical basis for the domain. Luckily, this is one of the few books that fill that gap. An excellent book, although the scope of numerical methods presented is limited.

More of a reference book. Doesn't go into much detail of each topic.

good and fast

A classic.

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Numerical Solution of Stochastic Differential Equations (Stochastic Modelling and Applied Probability) PDF

Numerical Solution of Stochastic Differential Equations (Stochastic Modelling and Applied Probability) PDF

Numerical Solution of Stochastic Differential Equations (Stochastic Modelling and Applied Probability) PDF

Numerical Solution of Stochastic Differential Equations (Stochastic Modelling and Applied Probability) PDF