Bayesian approaches for combining computational model output and physical observations

Wednesday, June 8, 2011 - 1:00pm - 2:00pm
Keller 3-180
David Higdon (Los Alamos National Laboratory)
A Bayesian formulation adapted from Kennedy and O'Hagan (2001) and
Higdon et al. (2008) is used to give parameter constraints from
physical observations and a limited number of simulations. The framework
is based on the idea of replacing the simulator by an emulator which
can then be used to facilitate computations required for the analysis.
In this talk I'll describe the details of this approach and apply it
to an example that uses large scale structure of the universe to
inform about a subset of the parameters controlling a
cosmological model. I'll also explain basics of using Gaussian
process models and compare them to an approach that uses the
ensemble Kalman filter.
MSC Code: