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This is a substantial expansion of the first edition. DRM-free; Included format: PDF; ebooks can be used on all reading devices; Immediate eBook download A highly readable introduction to stochastic integration and stochastic differential .
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It is written in a style suitable for the text of a graduate course in stochastic calculus, following a course in probability. For Brownian motion, the topics of local time, reflected Brownian motion, and time change are discussed. New to the second edition are a discussion of the Cameron—Martin—Girsanov transformation and a final chapter which provides an introduction to stochastic differential equations, as well as many exercises for classroom use.
This book will be a valuable resource to all mathematicians, statisticians, economists, and engineers employing the modern tools of stochastic analysis.
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I am sure that this book will be very welcomed by students and lectures of this subject Reader also should not miss out on the Preface, which includes some anecdotes about K. Country of Publication: US Dimensions cm : Help Centre.
My Wishlist Sign In Join. Be the first to write a review. Add to Wishlist. Ships in 15 business days. Link Either by signing into your account or linking your membership details before your order is placed. Description Table of Contents Product Details Click on the cover image above to read some pages of this book! Introduction Brownian motion Constructions of Brownian motion Stochastic integrals An extentions of stochastic integrals Stochastic integrals for martingales The Ito formula Multiple Wiener integrals Stochastic differential equations Applications to finance References Table of Contents provided by Publisher.
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Statistics for The Behavioral Sciences 10th Edition. Needless to say, there are scattered over the text minor improvements and corrections to the first edition. A Russian translation of the latter, without changes, appeared in Stochastic integration has grown in both theoretical and applicable importance in the last decade, to the extent that this new tool is now sometimes employed without heed to its rigorous requirements.
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This is no more surprising than the way mathematical analysis was used historically. We hope this modest introduction to the theory and application of this new field may serve as a text at the beginning graduate level, much as certain standard texts in analysis do for the deterministic counterpart. No monograph is worthy of the name of a true textbook without exercises.
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We have compiled a collection of these, culled from our experiences in teaching such a course at Stanford University and the University of California at San Diego, respectively. A highly readable introduction to stochastic integration and stochastic differential equations, this book combines developments of the basic theory with applications. It is written in a style suitable for the text of a graduate course in stochastic calculus, following a course in probability.
For Brownian motion, the topics of local time, reflected Brownian motion, and time change are discussed. New to the second edition are a discussion of the Cameron-Martin-Girsanov transformation and a final chapter which provides an introduction to stochastic differential equations, as well as many exercises for classroom use. This book will be a valuable resource to all mathematicians, statisticians, economists, and engineers employing the modern tools of stochastic analysis.
The text also proves that stochastic integration has made an important impact on mathematical progress over the last decades and that stochastic calculus has become one of the most powerful tools in modern probability theory. An attractive text Especially pleasant are the care and attention devoted to details A very fine book.