An Introduction to Markov Processes

Daniel W. Stroock

ISBN 10: 3642405223 / ISBN 13: 9783642405228

Published by Springer Berlin Heidelberg, 2013

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book provides a rigorous but elementary introduction to the theory of Markov Processes on a countable state space. It should be accessible to students with a solid undergraduate background in mathematics, including students from engineering, economics, physics, and biology. Topics covered are: Doeblin's theory, general ergodic properties, and continuous time processes. Applications are dispersed throughout the book. In addition, a whole chapter is devoted to reversible processes and the use of their associated Dirichlet forms to estimate the rate of convergence to equilibrium. These results are then applied to the analysis of the Metropolis (a.k.a simulated annealing) algorithm.The corrected and enlarged 2nd edition contains a new chapter in which the author develops computational methods for Markov chains on a finite state space. Most intriguing is the section with a new technique for computing stationary measures, which is applied to derivations of Wilson's algorithm and Kirchoff's formula for spanning trees in a connected graph.

Seller Inventory # 9783642405228

Bibliographic Details

• Title: An Introduction to Markov Processes
• Publisher: Springer Berlin Heidelberg
• Publication Date: 2013
• Language: English
• ISBN 10: 3642405223
• ISBN 13: 9783642405228
• Binding: Buch
• Edition: 2nd Edition
• Condition: Neu

About this title

Synopsis

This book provides a rigorous but elementary introduction to the theory of Markov Processes on a countable state space. It should be accessible to students with a solid undergraduate background in mathematics, including students from engineering, economics, physics, and biology. Topics covered are: Doeblin's theory, general ergodic properties, and continuous time processes. Applications are dispersed throughout the book. In addition, a whole chapter is devoted to reversible processes and the use of their associated Dirichlet forms to estimate the rate of convergence to equilibrium. These results are then applied to the analysis of the Metropolis (a.k.a simulated annealing) algorithm.

The corrected and enlarged 2^nd edition contains a new chapter in which the author develops computational methods for Markov chains on a finite state space. Most intriguing is the section with a new technique for computing stationary measures, which is applied to derivations of Wilson's algorithm and Kirchoff's formula for spanning trees in a connected graph.

About the Author

Daniel Stroock has held positions at NYU, the University of Colorado, and MIT. In addition, he has visited and lectured at many universities throughout the world. He has authored several books on analysis and various aspects of probability theory and their application to partial differential equations and differential geometry.