The Annals of Probability · 2009 · 94 citations · 19 references
Natural SciencesMathematicsProbability TheoryRepresentation TheoryStochastic ProcessesCharacteristic FunctionsGaussian ProcessesDeterminantal Point ProcessesFinite-dimensional Hilbert SpacesGaussian Random ProcessesRandom MatricesRandom Matrix TheoryReproducing Kernel Hilbert Spaces
We consider two families of random matrix-valued analytic functions: (1) G1−zG2 and (2) G0+zG1+z2G2+⋯, where Gi are n×n random matrices with independent standard complex Gaussian entries. The random set of z where these matrix-analytic functions become singular is shown to be determinantal point processes in the sphere and the hyperbolic plane, respectively. The kernels of these determinantal processes are reproducing kernels of certain Hilbert spaces (“Bargmann–Fock spaces”) of holomorphic functions on the corresponding surfaces. Along with the new results, this also gives a unified framework in which to view a theorem of Peres and Virág (n=1 in the second setting above) and a well-known result of Ginibre on Gaussian random matrices (which may be viewed as an analogue of our results in the whole plane).
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Statistical Ensembles of Complex, Quaternion, and Real Matrices
J. Ginibre · Journal of Mathematical Physics · 1965 · 1.1K citations
The coincidence approach to stochastic point processes
Odile Macchi · Advances in Applied Probability · 1975 · 590 citations
Engineering, Mathematics, Physics +13
Determinantal Processes and Independence
J. Hough, Manjunath Krishnapur, Yuval Peres et al. · Probability Surveys · 2006 · 514 citations · Full text