Logo

Reversible Markov Chains and Random Walks on Graphs

Reversible Markov Chains and Random Walks on Graphs
by

Publisher: University of California, Berkeley
Number of pages: 516

Description:
From the table of contents: General Markov Chains; Reversible Markov Chains; Hitting and Convergence Time, and Flow Rate, Parameters for Reversible Markov Chains; Special Graphs and Trees; Cover Times; Symmetric Graphs and Chains; Advanced L2 Techniques for Bounding Mixing Times; Some Graph Theory and Randomized Algorithms; Continuous State, Infinite State and Random Environment; Interacting Particles on Finite Graphs; Markov Chain Monte Carlo.

Home page url

Download or read it online for free here:
Download link
(1.8MB, PDF)

Similar books

Book cover: Inverse Problem Theory and Methods for Model Parameter EstimationInverse Problem Theory and Methods for Model Parameter Estimation
by - SIAM
The first part deals with discrete inverse problems with a finite number of parameters, while the second part deals with general inverse problems. The book for scientists and applied mathematicians facing the interpretation of experimental data.
(10755 views)
Book cover: Non-Uniform Random Variate GenerationNon-Uniform Random Variate Generation
by - Springer
The book on small field on the crossroads of statistics, operations research and computer science. The applications of random number generators are wide and varied. The study of non-uniform random variates is precisely the subject area of the book.
(9491 views)
Book cover: Markov Chains and Stochastic StabilityMarkov Chains and Stochastic Stability
by - Springer
The book on the theory of general state space Markov chains, and its application to time series analysis, operations research and systems and control theory. An advanced graduate text and a monograph treating the stability of Markov chains.
(15462 views)
Book cover: Markov Chains and Mixing TimesMarkov Chains and Mixing Times
by - American Mathematical Society
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.
(8641 views)