Statistics and Data Science Seminar
Ryan Martin
UIC
Recursive Bayes prediction with copulas
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.
Wednesday January 21, 2015 at 4:00 PM in SEO 636