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Operations for Learning with Graphical ModelsThis paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian net- works, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. These operations adapt existing techniques from statistics and automatic differentiation to graphs. Two standard algorithm schemes for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Some algorithms are developed in this graphical framework including a generalized version of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing some popular algorithms that fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.
Document ID
19990109135
Acquisition Source
Ames Research Center
Document Type
Other
Authors
Buntine, Wray L.
(NASA Ames Research Center Moffett Field, CA United States)
Date Acquired
August 19, 2013
Publication Date
November 1, 1994
Subject Category
Computer Programming And Software
Report/Patent Number
TR-IC-94-03
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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