- Series: Stochastic Modelling and Applied Probability (Book 23)
- Hardcover: 636 pages
- Publisher: Springer; Corrected edition (August 1992)
- Language: English
- ISBN-10: 3540540628
- ISBN-13: 978-3540540625
- Product Dimensions: 6.1 x 1.5 x 9.2 inches
- Shipping Weight: 2.4 pounds (View shipping rates and policies)
- Average Customer Review: 3.8 out of 5 stars See all reviews (7 customer reviews)
- Amazon Best Sellers Rank: #1,167,991 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Numerical Solution of Stochastic Differential Equations (Stochastic Modelling and Applied Probability) Corrected Edition
Use the Amazon App to scan ISBNs and compare prices.
Featured Springer resources in biomedicine
Explore these featured titles in biomedicine. Learn more
Frequently bought together
Customers who bought this item also bought
"... 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
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.
Browse award-winning titles. See more
If you are a seller for this product, would you like to suggest updates through seller support?
Top Customer Reviews
Generally I would not recommend this book, except as a reference for high-order simulation methods.