Reversible Markov Chains and Random Walks on Graphs

Reversible Markov Chains and Random Walks on Graphs

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

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: CK-12 Basic Probability and Statistics: A Short CourseCK-12 Basic Probability and Statistics: A Short Course
by - CK-12.org
CK-12 Foundation's Basic Probability and Statistics– A Short Course is an introduction to theoretical probability and data organization. Students learn about events, conditions, random variables, and graphs and tables that allow them to manage data.
Book cover: Probability and Statistics for Geophysical ProcessesProbability and Statistics for Geophysical Processes
by - National Technical University of Athens
Contents: The utility of probability; Basic concepts of probability; Elementary statistical concepts; Special concepts of probability theory in geophysical applications; Typical univariate statistical analysis in geophysical processes; etc.
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.
Book cover: Random Matrix Models and Their ApplicationsRandom Matrix Models and Their Applications
by - Cambridge University Press
The book covers broad areas such as topologic and combinatorial aspects of random matrix theory; scaling limits, universalities and phase transitions in matrix models; universalities for random polynomials; and applications to integrable systems.