Measure, integration & probability  Notes
Dr I F Wilde
Measure, integration & probability
(pdf file)
(78 pages, 381Kb)
These (draft) notes are based on lectures given some years ago at King's
College London (as part of the Mathematics BSc programme).
The material is mostly about measure and integration over a finite measure
space. (Probabilists are most insistent that their theory is more than just
measure theory with the total measure being equal to one.)
(Thanks to Sanne Zwart (Florence) for pointing out some misprints.)
Contents: 

σalgebras and Borel functions

Measures

Probability spaces, random variables and distribution
functions

Integration theory

Expectation in a probability space

Characteristic functions

Independence

Convergence of random variables

The strong law of large numbers

Stochastic processes


further available material.
Ivan F Wilde
email: iwilde (dot) mth (at) ntlworld (dot) com
<This page updated 14 June 2009>