[Ml-stat-talks] Fwd: Reminder: CSML Seminar: David Dunson, Tomorrow, January 19, 2016 at 12:30pm | Computer Science, Room 105
bee at princeton.edu
Mon Jan 18 09:37:48 EST 2016
Talk of interest tomorrow.
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David Dunson-Duke University
Tuesday, January 19, 2016
Computer Science, Room 105
**Lunch will be provided**
Title: “Probabilistic inference from complex and high-dimensional data”
Abstract: There is a well-known increasing trend in complexity and size of
data being collected across fields. Although many approaches have been
developed for analyzing such data, there is a clear lack of methods that
are robust, computationally scalable, and provide realistic uncertainty
quantification. We develop broad new tools for probabilistic inference
from big and complex data along several threads, including a new framework
for robust Bayesian inferences using coarsening, and new algorithms for
scalable computation relying on (i) breaking data into subsets, estimating
posterior probability measures for each subset and combining via
Wasserstein barycenters; and (ii) approximate Markov chain Monte Carlo
algorithms, which replace expensive transition kernels with approximations.
We discuss theoretical results on robustness, computational complexity, and
statistical accuracy and illustrate the methods with several applications.
If you would like to be added to the CSML listerv, please email
capizzi at princeton.edu.
Joseph D. Capizzi Jr.
Administrative Assistant to the Director
Center for Statistics and Machine Learning
Green Hall, 3-C-5
Princeton, NJ 08544
capizzi at princeton.edu
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