COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
Bulk scaling limit of the Laguerre ensembleAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Berestycki. Random matrix theory has found many applications in physics, statistics and engineering since its inception. The eigenvalues of random matrices are often of particular interest. The standard technique for studying local eigenvalue behavior of a random matrix distribution involves the following steps. We first choose a family of n x n random matrices which we translate and rescale in order to focus on a particular region of the spectrum, and then we let n \to\infty. When this procedure is performed carefully, the limiting eigenvalue behavior often falls into one of three classes: soft edge, hard edge or bulk. In the world of random matrices, three ensembles are of particular interest: the Hermite, Laguerre and Jacobi \beta-ensembles. In this talk I will present a joint work with Benedek Valkó. We consider the \beta-Laguerre ensemble, a family of distributions generalizing the joint eigenvalue distribution of the Wishart random matrices. We show that the bulk scaling limit of these ensembles exists for all \beta > 0 for a general family of parameters and it is the same as the bulk scaling limit of the corresponding \beta-Hermite ensemble. http://www.statslab.cam.ac.uk/Dept/People/jacquot.html This talk is part of the Probability series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsSpecial Astrophysics Seminars Cambridge Evolutionary Genetics Japanese Society in Cambridge ケンブリッジ日本人会Other talksTowards a whole brain model of perceptual learning Neurodevelopment disorders of genetic origin – what can we learn? Deep & Heavy: Using machine learning for boosted resonance tagging and beyond Succulents with Altitude Replication or exploration? Sequential design for stochastic simulation experiments An SU(3) variant of instanton homology for webs Art and Migration Constructing the virtual fundamental cycle Coin Betting for Backprop without Learning Rates and More Graded linearisations for linear algebraic group actions Value generalization during human avoidance learning |