Book contents
- Frontmatter
- Contents
- Preface to the second edition
- Preface to the first edition
- Some frequently used notations
- 1 Measure theory and probability
- Solutions for Chapter 1
- 2 Independence and conditioning
- Solutions for Chapter 2
- 3 Gaussian variables
- Solutions for Chapter 3
- 4 Distributional computations
- Solutions for Chapter 4
- 5 Convergence of random variables
- Solutions for Chapter 5
- 6 Random processes
- Solutions for Chapter 6
- Where is the notion N discussed?
- Final suggestions: how to go further?
- References
- Index
Preface to the first edition
Published online by Cambridge University Press: 05 August 2012
- Frontmatter
- Contents
- Preface to the second edition
- Preface to the first edition
- Some frequently used notations
- 1 Measure theory and probability
- Solutions for Chapter 1
- 2 Independence and conditioning
- Solutions for Chapter 2
- 3 Gaussian variables
- Solutions for Chapter 3
- 4 Distributional computations
- Solutions for Chapter 4
- 5 Convergence of random variables
- Solutions for Chapter 5
- 6 Random processes
- Solutions for Chapter 6
- Where is the notion N discussed?
- Final suggestions: how to go further?
- References
- Index
Summary
Originally, the main body of these exercises was developed for, and presented to, the students in the Magistère des Universités Parisiennes between 1984 and 1990; the audience consisted mainly of students from the Écoles Normales, and the spirit of the Magistère was to blend “undergraduate probability” (≟ random variables, their distributions, and so on …) with a first approach to “graduate probability” (≟ random processes). Later, we also used these exercises, and added some more, either in the Préparation à l'Agrégation de Mathématiques, or in more standard Master courses in probability.
In order to fit the exercises (related to the lectures) in with the two levels alluded to above, we systematically tried to strip a number of results (which had recently been published in research journals) of their random processes apparatus, and to exhibit, in the form of exercises, their random variables skeleton.
Of course, this kind of reduction may be done in almost every branch of mathematics, but it seems to be a quite natural activity in probability theory, where a random phenomenon may be either studied on its own (in a “small” probability world), or as a part of a more complete phenomenon (taking place in a “big” probability world); to give an example, the classical central limit theorem, in which only one Gaussian variable (or distribution) occurs in the limit, appears, in a number of studies, as a one-dimensional “projection” of a central limit theorem involving processes, in which the limits may be several Brownian motions, the former Gaussian variable appearing now as the value at time 1, say, of one of these Brownian motions.
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- Information
- Exercises in ProbabilityA Guided Tour from Measure Theory to Random Processes, via Conditioning, pp. xvii - xviiiPublisher: Cambridge University PressPrint publication year: 2012