Department Seminar Series: Nicholas Polson, Recursive Bayesian Computation


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  • Speaker: Nicholas Polson, PhD., Professor of Econometrics and Statistics, University of Chicago Booth School of Business
  • Host Department: Statistics
  • Date: 04/11/2014
  • Time: 11:30 AM

  • Location: 340 West Hall

  • Description:

    Abstract:  In this paper we develop a framework for recursive Bayesian computation. By exploiting an auxiliary latent variable structure we provide sequential parameter learning for a wide class of models. We illustrate our methodology with applications to high dimensional sparse regression, dynamic logistic classification, mixture Kalman filters and nonlinear and non-Gaussian state space models. The methods developed here are available in the package ParticleBayes.R.