Stochastic Calculus: Applications in Science and Engineering

Mircea Grigoriu

ISBN 10: 1461265010 / ISBN 13: 9781461265016

Published by Birkhaeuser, 2014

About this Item

reprint edition. 789 pages. 9.25x6.10x1.79 inches. In Stock.

Seller Inventory # x-1461265010

Bibliographic Details

• Title: Stochastic Calculus: Applications in Science and Engineering
• Publisher: Birkhaeuser
• Publication Date: 2014
• Language: English
• ISBN 10: 1461265010
• ISBN 13: 9781461265016
• Binding: Paperback
• Condition: Brand New
• Weight: 1.09 kilograms

About this title

Synopsis

"Stochastic Calculus" focuses on analyzing and presenting solutions for a wide range of stochastic problems in applied mathematics, probability theory, physics, science, engineering, and finance. The author outlines essentials of probability theory, random processes, stochastic integration, and Monte Carlo simulation, then presents methods for solving problems defined by equations with deterministic and/or random coefficients and deterministic and/or stochastic inputs. This self-contained text may be used for graduate courses and as a reference for applied scientists interested in methods for solving stochastic problems.

From the Back Cover

This work focuses on analyzing and presenting solutions for a wide range of stochastic problems that are encountered in applied mathematics, probability, physics, engineering, finance, and economics. The approach used reduces the gap between the mathematical and engineering literature. Stochastic problems are defined by algebraic, differential or integral equations with random coefficients and/or input. However, it is the type, rather than the particular field of application, that is used to categorize these problems.

An introductory chapter outlines the types of stochastic problems under consideration in this book and illustrates some of their applications. A user friendly, systematic exposition unfolds as follows:  The essentials of probability theory, random processes, stochastic integration, and Monte Carlo simulation are developed in chapters 2--5. The Monte Carlo method is used extensively to illustrate difficult theoretical concepts and solve numerically some ofthe stochastic problems in chapters 6--9.

Key features:

* Computational skills developed as needed to solve realistic stochastic problems

* Classical mathematical notation used, and essential theoretical facts boxed

* Numerous examples from applied sciences and engineering

* Complete proofs given; if too technical, notes clarify the idea and/or main steps

* Problems at the end of each chapter reinforce applications; hints given.

* Good bibliography at the end of every chapter

* Comprehensive index

This work is unique, self-contained, and far from a collection of facts and formulas. The analytical and numerical methods approach for solving stochastic problems may be used for self-study by a variety of researchers, and in the classroom by first year graduate students.