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What do Noisy Datapoints Tell Us About the True Signal?Charles HoggCeramics Division, NIST Tuesday, January 17, 2012 15:00-16:00, 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 explain how to 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. Speaker Bio: Dr. Charles R. ("Chip") Hogg obtained a M.S. and Ph.D. in Physics from Carnegie Mellon University in Pittsburgh, after earning a B.Sc. from Brock University in Canada with a double major in Computer Science and Physics. Since October 2010 he has been a Guest Researcher in the Ceramics Division at NIST, supported by a NIST-ARRA postdoctoral fellowship. He is broadly interested in applying Bayesian methods to the physical sciences; his recent work has heavily involved nonstationary Gaussian Processes with applications to local atomic structure determination.
Contact: I. Beichl Note: Visitors from outside NIST must contact Robin Bickel; (301) 975-3668; at least 24 hours in advance. |