A User's Guide to Measure Theoretic Probability

by David Pollard, Robin Gill (Editor), B.D. Ripley (Editor)

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.

Genres: Mathematics, Textbooks, Science

366 pages, Paperback

First published January 1, 2001

Reviews

[Leon · Rating 5 out of 5]

Absolute banger.

Not a single word or equation could be left out.

Best introduction to measure theoretic probability you’ll come across.