Model Theory of Stochastic Processes: Lecture Notes in Logic 14

by Sergio Fajardo, H. Jerome Keisler

Overview

This book presents new research in probability theory using ideas from mathematical logic. It is a general study of stochastic processes on adapted probability spaces, employing the concept of similarity of stochastic processes based on the notion of adapted distribution. The authors use ideas from model theory and methods from nonstandard analysis

Product Details

ISBN-13:	9781040190272
Publisher:	CRC Press
Publication date:	01/01/2002
Sold by:	Barnes & Noble
Format:	eBook
Pages:	140
File size:	483 KB

About the Author

Sergio Fajardo Department of Mathematics, University of Los Andes, Bogota, Colombia. H. Jerome Keisler Department of Mathematics, University of Wisconsin, Madison, Wisconsin, USA.

Table of Contents

Introduction Chapter 1. Adapted distributions Chapter 2. Hyperfnite adapted spaces Chapter 3. Saturated spaces Chapter 4. Comparing stochastic processes Chapter 5. Defnability in adapted spaces Chapter 6. Elementary extensions Chapter 7. Rich adapted spaces Chapter 8. Adapted neometric spaces Chapter 9. Enlarging saturated spaces

Editorial Reviews

Studies stochastic processes on adapted probability spaces using a concept of similarity of stochastic processes based on the notion of adapted distribution. Fajardo (University of Los Andes) and Keisler (University of Wisconsin) also address why there is a collection of results about stochastic processes which can only be proved by means of non-standard analysis involving saturation. Annotation c. Book News, Inc., Portland, OR