Markov Processes, Gaussian Processes, and Local Times (Hardcover)

Michael B. Marcus

ISBN 10: 0521863007 / ISBN 13: 9780521863001

Edité par Cambridge University Press, Cambridge, 2006

A propos de cet article

Hardcover. This book was first published in 2006. Written by two of the foremost researchers in the field, this book studies the local times of Markov processes by employing isomorphism theorems that relate them to certain associated Gaussian processes. It builds to this material through self-contained but harmonized 'mini-courses' on the relevant ingredients, which assume only knowledge of measure-theoretic probability. The streamlined selection of topics creates an easy entrance for students and experts in related fields. The book starts by developing the fundamentals of Markov process theory and then of Gaussian process theory, including sample path properties. It then proceeds to more advanced results, bringing the reader to the heart of contemporary research. It presents the remarkable isomorphism theorems of Dynkin and Eisenbaum and then shows how they can be applied to obtain new properties of Markov processes by using well-established techniques in Gaussian process theory. This original, readable book will appeal to both researchers and advanced graduate students. Written by two foremost researchers in the field, this book studies the local times of Markov processes by employing isomorphism theorems that relate them to certain associated Gaussian processes. It builds to this material through self-contained but harmonized 'mini-courses' on the relevant ingredients, which assume only knowledge of measure-theoretic probability. The streamlined selection of topics creates an easy entrance for students and for experts in related fields. The book starts by developing the fundamentals of Markov process theory and then of Gaussian process theory, including sample path properties. It then proceeds to more advanced results, bringing the reader to the heart of contemporary research. It presents the remarkable isomorphism theorems of Dynkin and Eisenbaum, then shows how they can be applied to obtain new properties of Markov processes by using well-established techniques in Gaussian process theory. This original, readable book will appeal to both researchers and advanced graduate students. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

N° de réf. du vendeur 9780521863001

Détails bibliographiques

• Titre: Markov Processes, Gaussian Processes, and Local Times (Hardcover)
• Éditeur: Cambridge University Press, Cambridge
• Date de parution: 2006
• Langue: anglais
• ISBN à 10 chiffres: 0521863007
• ISBN à 13 chiffres: 9780521863001
• Reliure: Hardcover
• Édition: Edition originale
• État de l'article: new

A propos de ce titre

Synopsis

This book was first published in 2006. Written by two of the foremost researchers in the field, this book studies the local times of Markov processes by employing isomorphism theorems that relate them to certain associated Gaussian processes. It builds to this material through self-contained but harmonized 'mini-courses' on the relevant ingredients, which assume only knowledge of measure-theoretic probability. The streamlined selection of topics creates an easy entrance for students and experts in related fields. The book starts by developing the fundamentals of Markov process theory and then of Gaussian process theory, including sample path properties. It then proceeds to more advanced results, bringing the reader to the heart of contemporary research. It presents the remarkable isomorphism theorems of Dynkin and Eisenbaum and then shows how they can be applied to obtain new properties of Markov processes by using well-established techniques in Gaussian process theory. This original, readable book will appeal to both researchers and advanced graduate students.

À propos de l?auteur

Michael B. Marcus is Professor of Mathematics at City College and The CUNY Graduate Center. A leading expert on stochastic processes, he has published over one hundred research papers and delivered over 200 invited lectures. He is a Fellow of the Institute of Mathematical Statistics. Jay Rosen is Professor of Mathematics at The Graduate Center and the College of Staten Island, City University of New York. A leading expert on stochastic processes, he has published over eighty research papers. He is a Fellow of the Institute of Mathematical Statistics.