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     COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER
AND THE SCHOOL OF PHYSICS, ASTRONOMY, & COMPUTATIONAL SCIENCES
                            (CSI 898-Sec 001)
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     What do noisy datapoints tell us about the true signal?

                           Charles Hogg III
  National Institute of Standards and Technology, Gaithersburg, MD

 Every measurement has uncertainty which needs to be quantified.
Bayesian approaches achieve this naturally, by expressing results in
terms of probabilities.  I will give a conceptual overview of Bayesian
analysis for metrological applications.  This includes a discussion of
Occam's razor, a helpful but qualitative dictum that is clarified and
quantified when recast in the language of probability.  Three example
systems will illustrate these concepts: finding the true X-ray
diffraction curve from noisy count data, interpolating the strain
field of a stretched metal plate, and measuring aggregate uncertainty
in flame speed datasets.  All these systems require us to calculate
probabilities for arbitrary smooth functions without assuming a
functional form, and I will illustrate one tool (Gaussian Processes)
which lets us do this in a Bayesian context.  Having quantified the
uncertainty, I will also show several ways to represent it, including
smooth animations of sequences of candidates for the true signal.

                          October 15, 2012
                               4:30 pm
                    Room 301, Research I, Fairfax Campus

Refreshments will be served at 4:15 PM.
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Find the schedule at http://www.cmasc.gmu.edu/seminars.htm