Y
Yuval Peres
Researcher at Microsoft
Publications - 642
Citations - 23986
Yuval Peres is an academic researcher from Microsoft. The author has contributed to research in topics: Random walk & Hausdorff dimension. The author has an hindex of 72, co-authored 637 publications receiving 22004 citations. Previous affiliations of Yuval Peres include Tel Aviv University & Chalmers University of Technology.
Papers
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Book
Markov Chains and Mixing Times
TL;DR: Markov Chains and Mixing Times as mentioned in this paper is an introduction to the modern approach to the theory of Markov chains and its application in the field of probability theory and linear algebra, where the main goal is to determine the rate of convergence of a Markov chain to the stationary distribution.
Book
Probability on Trees and Networks
Russell Lyons,Yuval Peres +1 more
TL;DR: In this article, the authors present a state-of-the-art account of probability on networks, including percolation, isoperimetric inequalities, eigenvalues, transition probabilities, and random walks.
Journal ArticleDOI
Determinantal Processes and Independence
TL;DR: In this paper, the authors give a probabilistic introduction to determinantal and per-manental point processes and establish analogous representations for permanental pro- cesses, with geometric variables replacing the Bernoulli variables.
MonographDOI
Zeros of Gaussian Analytic Functions and Determinantal Point Processes
TL;DR: The book examines in some depth two important classes of point processes, determinantal processes and 'Gaussian zeros', i.e., zeros of random analytic functions with Gaussian coefficients, which share a property of 'point-repulsion', and presents a primer on modern techniques on the interface of probability and analysis.
Journal ArticleDOI
Conceptual proofs of L log L criteria for mean behavior of branching processes
TL;DR: The Kesten-Stigum theorem is a fundamental criterion for the rate of growth of a supercritical branching process, showing that an $L \log L$ condition is decisive as mentioned in this paper.