Markov Processes, Gaussian Processes, and Local Times: 100 (Cambridge Studies in Advanced Mathematics, Series Number 100) - Image 1

£83.19

Cambridge University Press Markov Processes, Gaussian Processes, and Local Times: 100 (Cambridge Studies in Advanced Mathematics, Series Number 100)

Name: Markov Processes, Gaussian Processes, and Local Times: 100 (Cambridge Studies in Advanced Mathematics, Series Number 100)
Brand: Cambridge University Press
Price: 83.19 GBP
Availability: InStock

Electronics & Communications Engineering

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Description

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.

Product Specifications

Brand: Cambridge University Press
Format: hardcover
ASIN: 0521863007
Category: Books > Subjects > Science, Nature & Maths > Engineering & Technology > Electronics & Communications Engineering
Domain: Amazon UK
Release Date: 24 July 2006
Listed Since: 05 February 2007

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Cambridge University Press Markov Processes, Gaussian Processes, and Local Times: 100 (Cambridge Studies in Advanced Mathematics, Series Number 100)

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