Logo

Markov Chains and Mixing Times

Large book cover: Markov Chains and Mixing Times

Markov Chains and Mixing Times
by

Publisher: American Mathematical Society
ISBN/ASIN: 0821847392
ISBN-13: 9780821847398
Number of pages: 387

Description:
This book is an introduction to the modern approach to the theory of Markov chains. The main goal of this approach is to determine the rate of convergence of a Markov chain to the stationary distribution as a function of the size and geometry of the state space. The authors develop the key tools for estimating convergence times, including coupling, strong stationary times, and spectral methods.

Home page url

Download or read it online for free here:
Download link
(4.5MB, PDF)

Similar books

Book cover: An Introduction to Stochastic PDEsAn Introduction to Stochastic PDEs
by - arXiv
This text is an attempt to give a reasonably self-contained presentation of the basic theory of stochastic partial differential equations, taking for granted basic measure theory, functional analysis and probability theory, but nothing else.
(12874 views)
Book cover: Lectures on Probability, Statistics and EconometricsLectures on Probability, Statistics and Econometrics
by - statlect.com
This e-book is organized as a website that provides access to a series of lectures on fundamentals of probability, statistics and econometrics, as well as to a number of exercises on the same topics. The level is intermediate.
(13630 views)
Book cover: Seeing Theory: A visual introduction to probability and statisticsSeeing Theory: A visual introduction to probability and statistics
by - Brown University
The intent of the website and these notes is to provide an intuitive supplement to an introductory level probability and statistics course. The level is also aimed at students who are returning to the subject and would like a concise refresher ...
(7379 views)
Book cover: Bayesian Field TheoryBayesian Field Theory
by - arXiv.org
A particular Bayesian field theory is defined by combining a likelihood model, providing a probabilistic description of the measurement process, and a prior model, providing the information necessary to generalize from training to non-training data.
(5766 views)