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DTSTAMP:20250806T150413Z
DESCRIPTION:Title: Quasi-random sampling for multivariate distributions via
  generative neural networks\n\n\n	Abstract\n\n\nA novel approach based on g
 enerative neural networks is introduced for constructing quasi-random numb
 er generators for multivariate models with any underlying copula in order 
 to estimate expectations with variance reduction. So far\, quasi-random nu
 mber generators for multivariate distributions required a careful design\,
  exploiting specific properties (such as conditional distributions) of the
  implied copula or the underlying quasi-Monte Carlo point set\, and were o
 nly tractable for a small number of models. Utilizing specific generative 
 neural networks allows one to construct quasi-random number generators for
  a much larger variety of multivariate distributions without such restrict
 ions. Once trained with a pseudo-random sample\, these neural networks onl
 y require a multivariate standard uniform randomized quasi-Monte Carlo poi
 nt set as input and are thus fast in estimating expectations under depende
 nce with variance reduction. Reproducible numerical examples are considere
 d to demonstrate the approach. Emphasis is put on ideas rather than mathem
 atical proofs.\n\n\n	Speaker\n\n\nMarius Hofert is an Associate Professor o
 f Statistics in the Department of Statistics and Actuarial Science at Univ
 ersity of Waterloo\, Canada. He obtained his PhD in Mathematics from Unive
 rsity of Ulm in 2010. He then held a postdoctoral research position at Ris
 kLab\, ETH Zürich. Before joining University of Waterloo\, he had a guest 
 professorship in the Department of Mathematics at Technische Universität M
 ünchen and a visiting assistant professorship in the Department of Applied
  Mathematics at University of Washington\, Seattle. Marius’ research inter
 ests are Computational Statistics and Data Science (data visualization\, p
 arallel computing\, software development in R)\, Dependence Modeling with 
 Copulas (high dimensional problems\, hierarchical models\, random number g
 eneration\, computational aspects\, graphical approaches) and Quantitative
  Risk Management (risk aggregation\, risk measures\, computational challen
 ges).\n\nZoom Link\n\nMeeting ID: 924 5390 4989\n\nPasscode: 690084\n\n \n
DTSTART:20201204T203000Z
DTEND:20201204T213000Z
SUMMARY:Marius Hofert (University of Waterloo)
URL:/mathstat/channels/event/marius-hofert-university-
 waterloo-326639
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