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: 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.
(8236 views)
Book cover: A defense of Columbo: A multilevel introduction to probabilistic reasoningA defense of Columbo: A multilevel introduction to probabilistic reasoning
by - arXiv
Triggered by a recent interesting article on the too frequent incorrect use of probabilistic evidence in courts, the author introduces the basic concepts of probabilistic inference with a toy model, and discusses several important issues.
(10600 views)
Book cover: Introduction Probaility and StatisticsIntroduction Probaility and Statistics
by - University of Southern Maine
Topics: Data Analysis; Probability; Random Variables and Discrete Distributions; Continuous Probability Distributions; Sampling Distributions; Point and Interval Estimation; Large Sample Estimation; Large-Sample Tests of Hypothesis; etc.
(21020 views)
Book cover: Lectures on Stochastic AnalysisLectures on Stochastic Analysis
by - University of Wisconsin
Covered topics: stochastic integrals with respect to general semimartingales, stochastic differential equations based on these integrals, integration with respect to Poisson measures, stochastic differential equations for general Markov processes.
(9014 views)