B
Bálint Virág
Researcher at University of Toronto
Publications - 123
Citations - 4630
Bálint Virág is an academic researcher from University of Toronto. The author has contributed to research in topics: Random walk & Random matrix. The author has an hindex of 31, co-authored 121 publications receiving 4062 citations. Previous affiliations of Bálint Virág include University of Colorado Boulder & Alfréd Rényi Institute of Mathematics.
Papers
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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
Beta ensembles, stochastic Airy spectrum, and a diffusion
TL;DR: In this paper, it was shown that the largest eigenvalues of the beta ensembles of random matrix theory converge in distribution to the low-lying eigen values of the random Schrodinger operator − d2 dx2 + x+ 2 √ β bx restricted to the positive half-line.
Journal ArticleDOI
Continuum limits of random matrices and the Brownian carousel
Benedek Valkó,Bálint Virág +1 more
TL;DR: The authors showed that at any location away from the spectral edge, the eigenvalues of the Gaussian unitary ensemble and its general β siblings converge to Sine β, a translation invariant point process.
Journal ArticleDOI
Zeros of the i.i.d. Gaussian power series: a conformally invariant determinantal process
Yuval Peres,Bálint Virág +1 more
TL;DR: In this paper, it was shown that the zero set of the random power series f(z) = P anz n with i.i.d. complex Gaussian coefficientsan.