Greedy structure learning of Markov Random Fields

Greedy structure learning of Markov Random Fields

Probabilistic graphical models are used in a variety of domains to capture and represent general dependencies in joint probability distributions. In this document we examine the problem of learning the structure of an undirected graphical model, also called a Markov Random Field (MRF), given a set...

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Bibliographic Details
Main Author: Johnson, Christopher Carroll
Format: Others
Language:English
Published: 2011
Subjects:
Machine learning
Graphical models
Markov Random Fields
Structure learning
Probability
Uncertainty
Greedy algorithms
Online Access:http://hdl.handle.net/2152/ETD-UT-2011-08-4331

Internet

http://hdl.handle.net/2152/ETD-UT-2011-08-4331

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