Department seminar by Rui Tuo, Georgia Institute of TechnologyExport this event to calendar

Wednesday, November 1, 2017 — 9:00 AM EDT

A new framework of calibration for computer models: parameterization and efficient estimation

In this talk I will show some theoretical advances on the problem of calibration for computer models. The goal of calibration is to identify the model parameters in deterministic computer experiments, which cannot be measured or are not available in physical experiments. A theoretical framework is given which enables the study of parameter identifiability and estimation. In a study of the prevailing Bayesian method proposed by Kennedy and O’Hagan (2001), Tuo-Wu (2015, 2016) and Tuo-Wang-Wu (2017) find that this method may render unreasonable estimation for the calibration parameters. A novel calibration method, called L2 calibration, is proposed and proven to enjoy nice asymptotic properties, including asymptotic normality and semi-parametric efficiency. Inspired by a new advance in Gaussian process modeling, called orthogonal Gaussian process models (Plumlee and Joseph, 2016, Plumlee 2016), I have proposed another methodology for calibration. This new method is proven to be semi-parametric efficient, and in addition it allows for a simple Bayesian version so that Bayesian uncertainty quantification can be carried out computationally. In some sense, this latest work provides a complete solution to a long-standing problem in uncertainty quantification (UQ).

Location 
M3 - Mathematics 3
Room: 3127
200 University Avenue West
Waterloo, ON N2L 3G1
Canada

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