For more information about this meeting, contact Jason Morton, Manfred Denker.
| Title: | Identifiable Gaussian Graphical Models |
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| Seminar: | Seminar on Probability and its Application |
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| Speaker: | Seth Sullivant, North Carolina State University |
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| Abstract: |
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| A Gaussian graphical model is a statistical model that uses a
graph to encode linear relationships between a collection of random
variables. These linear relationships determine a positive definite
covariance matrix whose entries are polynomials in certain parameters
associated to the edges of the graph. The edge parameters are identifiable
if they can be recovered from the covariance matrix. I will discuss recent
results, where we employ techniques from computational algebra and
algebraic combinatorics to make progress on the identifiability problem.
Algebraic background will be kept a minimum. This is joint work with
Mathias Drton, Rina Foygel, Luis Garcia, and Sarah Spiegelvogel. |
Room Reservation Information
| Room Number: | MB106 |
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| Date: | 11 / 12 / 2010 |
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| Time: | 02:30pm - 03:25pm |
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