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Item specifics
- Condition
- ISBN-13
- 9781118166406
- Book Title
- A Modern Theory of Random Variation
- ISBN
- 9781118166406
- Subject Area
- Mathematics, Science, Business & Economics
- Publication Name
- Modern Theory of Random Variation : With Applications in Stochastic Calculus, Financial Mathematics, and Feynman Integration
- Publisher
- Wiley & Sons, Incorporated, John
- Item Length
- 9.1 in
- Subject
- Probability & Statistics / General, Calculus, Econometrics, Physics / Mathematical & Computational, Statistics, Mathematical Analysis
- Publication Year
- 2012
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.3 in
- Item Weight
- 30.5 Oz
- Item Width
- 6.3 in
- Number of Pages
- 544 Pages
A Modern Theory of Random Variation: With Applications in Stochastic Calculus, F
About this product
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
111816640X
ISBN-13
9781118166406
eBay Product ID (ePID)
25038772101
Product Key Features
Number of Pages
544 Pages
Publication Name
Modern Theory of Random Variation : With Applications in Stochastic Calculus, Financial Mathematics, and Feynman Integration
Language
English
Publication Year
2012
Subject
Probability & Statistics / General, Calculus, Econometrics, Physics / Mathematical & Computational, Statistics, Mathematical Analysis
Type
Textbook
Subject Area
Mathematics, Science, Business & Economics
Format
Hardcover
Dimensions
Item Height
1.3 in
Item Weight
30.5 Oz
Item Length
9.1 in
Item Width
6.3 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2012-002023
Dewey Edition
23
TitleLeading
A
Illustrated
Yes
Dewey Decimal
519.2/3
Table Of Content
Preface xi Symbols xiii 1 Prologue 1 2 Introduction 37 3 Infinite-Dimensional Integration 83 4 Theory of the Integral 111 5 Random Variability 183 6 Gaussian Integrals 257 7 Brownian Motion 305 8 Stochastic Integration 383 9 Numerical Calculation 447 A Epilogue 491 Bibliography 505 Index 521
Synopsis
A ground-breaking and practical treatment of probability and stochastic processes A Modern Theory of Random Variation is a new and radical re-formulation of the mathematical underpinnings of subjects as diverse as investment, communication engineering, and quantum mechanics. Setting aside the classical theory of probability measure spaces, the book utilizes a mathematically rigorous version of the theory of random variation that bases itself exclusively on finitely additive probability distribution functions. In place of twentieth century Lebesgue integration and measure theory, the author uses the simpler concept of Riemann sums, and the non-absolute Riemann-type integration of Henstock. Readers are supplied with an accessible approach to standard elements of probability theory such as the central limmit theorem and Brownian motion as well as remarkable, new results on Feynman diagrams and stochastic integrals. Throughout the book, detailed numerical demonstrations accompany the discussions of abstract mathematical theory, from the simplest elements of the subject to the most complex. In addition, an array of numerical examples and vivid illustrations showcase how the presented methods and applications can be undertaken at various levels of complexity. A Modern Theory of Random Variation is a suitable book for courses on mathematical analysis, probability theory, and mathematical finance at the upper-undergraduate and graduate levels. The book is also an indispensible resource for researchers and practitioners who are seeking new concepts, techniques and methodologies in data analysis, numerical calculation, and financial asset valuation. Patrick Muldowney, PhD, served as lecturer at the Magee Business School of the UNiversity of Ulster for over twenty years. Dr. Muldowney has published extensively in his areas of research, including integration theory, financial mathematics, and random variation., A groundbreaking and practical treatment of probability and stochastic processes A Modern Theory of Random Variation presents a new and radical reformulation of the mathematical underpinnings of subjects as diverse as investment, communication engineering, and quantum mechanics. Setting aside the classical theory of probability measure spaces, the book utilizes a mathematically rigorous version of the theory of random variation based exclusively on finitely additive probability distribution functions. In place of twentieth-century Lebesgue integration and measure theory, the author uses the simpler concept of Riemann sums and the non-absolute Riemann-type integration of Henstock. Readers are supplied with an accessible approach to standard elements of probability theory such as the central limit theorem and Brownian motion as well as remarkable, new results on Feynman diagrams and stochastic integrals. Detailed numerical examples and demonstrations guide the reader through the abstract mathematical exposition. In addition, numerous diagrams and graphics provide vivid illustrations of the theory, from the elementary level to the more advanced. A Modern Theory of Random Variation is suitable for courses on mathematical analysis, probability theory, and mathematical finance at the upper-undergraduate and graduate levels. The book is also an indispensable resource for researchers and practitioners who are seeking new concepts, techniques, and methodologies in data analysis, numerical calculation, and financial asset valuation., With a rigorous theorem-proof approach to stochastic models for financial mathematics as well as a unique focus on Feynman path integration, this book presents the theory of random processes and has applications in numerous areas including applied mathematics and statistics, finance, communication engineering, quantum mechanics, and physics.
LC Classification Number
QA273.M864 2012
Price : 202.89
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