Hardness of Parameter Estimation in Graphical Models

Hardness of Parameter Estimation in Graphical Models

We consider the problem of learning the canonical parameters specifying an undirected graphical model (Markov random field) from the mean parameters. For graphical models representing a minimal exponential family, the canonical parameters are uniquely determined by the mean parameters, so the proble...

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Bibliographic Details
Main Authors: Bresler, Guy (Contributor), Gamarnik, David (Contributor), Shah, Devavrat (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor), Sloan School of Management (Contributor)
Format: Article
Language:English
Published: Neural Information Processing Systems Foundation, 2016-02-02T00:30:10Z.
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