TITLE: Bayesian Computation Using Design of Experiments-based Interpolation
Technique

SPEAKER: Dr. Roshan Vengazhiyil

ABSTRACT:

A new deterministic approximation method for Bayesian computation, known
as Design of Experiments-based Interpolation Technique (DoIt), is
proposed. The method works by sampling points from the parameter space
using an experimental design and then fitting a kriging model to
interpolate the unnormalized posterior. The approximated posterior
density is a weighted average of normal densities and therefore, most of
the posterior quantities can be easily computed. DoIt is a general
computing technique which is easy-to-implement and can be applied to
many complex Bayesian problems. Moreover, it does not suffer from the
curse of dimensionality as much as some of the quadrature methods. It
can work using fewer posterior evaluations, which is a great advantage
over the Monte Carlo and Markov chain Monte Carlo methods especially
when dealing with computationally expensive posteriors.