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Spring 2012 GRASP Seminar – Emily B. Fox, University of Pennsylvania, “Bayesian Nonparametric Methods for Complex Dynamical Phenomena”

March 2, 2012 @ 11:00 am - 12:00 pm

Abstract: Markov switching processes, such as hidden Markov models (HMMs) and
switching linear dynamical systems (SLDSs), are often used to describe
rich classes of dynamical phenomena.  They describe complex temporal
behavior via repeated returns to a set of simpler models: imagine, for
example, a person alternating between walking, running and jumping
behaviors, or a stock index switching between regimes of high and low

Traditional modeling approaches for Markov switching processes
typically assume a fixed, pre-specified number of dynamical models.
 Here, in contrast, I develop Bayesian nonparametric approaches that
define priors on an unbounded number of potential Markov models.  Using
stochastic processes including the beta and Dirichlet process, I
develop methods that allow the data to define the complexity of
inferred classes of models, while permitting efficient computational
algorithms for inference.  The new methodology also has generalizations
for modeling and discovery of dynamic structure shared by multiple
related time series.

Interleaved throughout the talk are results from studies of the NIST
speaker diarization database, stochastic volatility of a stock index,
the dances of honeybees, and human motion capture videos.


- Learn More

Emily B. Fox received the S.B. degree in 2004, M.Eng. degree in 2005,
and E.E. degree in 2008 from the Department of Electrical Engineering
and Computer Science at the Massachusetts Institute of Technology
(MIT). She is currently an assistant professor in the Wharton
Statistics Department at the University of Pennsylvania. Her Ph.D. was
advised by Prof. Alan Willsky in the Stochastic Systems Group, and she
recently completed a postdoc in the Department of Statistical Science
at Duke University working with Profs. Mike West and David Dunson.
Emily is a recipient of the National Defense Science and Engineering
Graduate (NDSEG) and National Science Foundation (NSF) Graduate
Research fellowships, and currently holds an NSF Mathematical Sciences
Postdoctoral Research Fellowship. She has also been awarded the 2009
Leonard J. Savage Thesis Award in Applied Methodology, the 2009 MIT
EECS Jin-Au Kong Outstanding Doctoral Thesis Prize, the 2005 Chorafas
Award for superior contributions in research, and the 2005 MIT EECS
David Adler Memorial 2nd Place Master’s Thesis Prize. Her research
interests are in multivariate time series analysis and Bayesian
nonparametric methods.


March 2, 2012
11:00 am - 12:00 pm
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