Model theory of stochastic processes

Lecture notes in logic

Sergio Fajardo, Jerome H. Keisler

148 pages, parution le 26/06/2003

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Résumé

This book studies stochastic processes using ideas from model theory. Some key tools come from nonstandard analysis. It is written for readers from each of these three areas. We begin by intuitively describing this work from each of the three viewpoints.

From the viewpoint of probability theory, this is a general study of stochastic processes on adapted spaces based on the notion of adapted distribution. This notion is the analog for adapted spaces of the finite dimensional distribution, and was introduced by Hoover and Keisler [1984]. It gives us a way of comparing stochastic processes even if they are defined on different adapted spaces. A central theme will be the question
When are two stochastic processes alike?

Introduction
Adapted distributions
Hyperfinite adapted spaces
Saturated spaces
Comparing stochastic processes
Defnability in adapted spaces
Elementary extensions
Rich adapted spaces
Adapted neometric spaces
Enlarging saturated spaces

References

Caractéristiques techniques

	PAPIER
Éditeur(s)	AK Peters
Auteur(s)	Sergio Fajardo, Jerome H. Keisler
Parution	26/06/2003
Nb. de pages	148
Format	16,5 x 23,5
Couverture	Broché
Poids	235g
Intérieur	Noir et Blanc
EAN13	9781568811727
ISBN13	978-1-56881-172-7