Statistics Seminar - Bayesian Computation Using Design of Experiments-based Interpolation Technique

Event Details
  • Date/Time:
    • Friday March 16, 2012
      3:00 pm - 4:00 pm
  • Location: ISyE Executive Classroom
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Summary Sentence: Bayesian Computation Using Design of Experiments-based Interpolation Technique

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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.

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School of Industrial and Systems Engineering (ISYE)

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Seminar/Lecture/Colloquium
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Status
  • Created By: Anita Race
  • Workflow Status: Published
  • Created On: Mar 12, 2012 - 11:33am
  • Last Updated: Oct 7, 2016 - 9:58pm