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:

  1. σ-algebras and Borel functions
  2. Measures
  3. Probability spaces, random variables and distribution functions
  4. Integration theory
  5. Expectation in a probability space
  6. Characteristic functions
  7. Independence
  8. Convergence of random variables
  9. The strong law of large numbers
  10. Stochastic processes

- further available material.



Ivan F Wilde
e-mail: iwilde (dot) mth (at) ntlworld (dot) com
<This page updated 14 June 2009>