Ryan Martin : Recursive Bayes prediction with copulas

Posted by Ryan Martin , part of the Statistics and Data Science Seminar.

At

Jan. 21, 2015, 4 p.m.

In

SEO 636

Abstract

The Bayesian framework provides a nice recipe for constructing the predictive distribution of a future observation given the available data. Except for simple problems, computation of the Bayes predictive requires Monte Carlo which cannot be done recursively. However, when data is received sequentially, e.g., in finance applications, a recursive update to the predictive distribution is desired. In this talk, I will explain how the Bayes predictive step can be rewritten using a copula, which makes recursive updates of the predictive distribution possible. This new representation motivates a version of Newton's predictive recursion algorithm for the predictive density, which can be used for fast and universal recursive predictive density estimation. Illustrations and convergence theory for the new algorithm is provided.