A User's Guide to Measure Theoretic Probability / Edition 1 by David Pollard | 9780521002899 | Paperback | Barnes & Noble
A User's Guide to Measure Theoretic Probability / Edition 1

A User's Guide to Measure Theoretic Probability / Edition 1

by David Pollard
     
 

ISBN-10: 0521002893

ISBN-13: 9780521002899

Pub. Date: 12/10/2001

Publisher: Cambridge University Press

This book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous

Overview

This book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form. It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean.

Product Details

ISBN-13:
9780521002899
Publisher:
Cambridge University Press
Publication date:
12/10/2001
Series:
Cambridge Series in Statistical and Probabilistic Mathematics Series, #8
Edition description:
New Edition
Pages:
366
Product dimensions:
6.97(w) x 9.96(h) x 0.75(d)

Table of Contents

1. Motivation; 2. A modicum of measure theory; 3. Densities and derivatives; 4. Product spaces and independence; 5. Conditioning; 6. Martingale et al; 7. Convergence in distribution; 8. Fourier transforms; 9. Brownian motion; 10. Representations and couplings; 11. Exponential tails and the law of the iterated logarithm; 12. Multivariate normal distributions; Appendix A. Measures and integrals; Appendix B. Hilbert spaces; Appendix C. Convexity; Appendix D. Binomial and normal distributions; Appendix E. Martingales in continuous time; Appendix F. Generalized sequences.

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