DocumentCode
1312918
Title
Dynamic Graphical Models
Author
Bilmes, Jeff
Author_Institution
An associate professor in the Department of Electrical Engineering at the University of Washington, Seattle, and is an adjunct professor in the Department of Computer Science and Engineering and the Department of Linguistics.
Volume
27
Issue
6
fYear
2010
Firstpage
29
Lastpage
42
Abstract
A graphical model consists of I a graph G = (V,E) and a V set of properties that determine a family of V probability distributions. There are many different types of graphs and properties, each determining a family. It is common to be able to develop algorithms that work for all members of the family by considering only a graph and its properties. Thus, solving difficult problems (such as deriving an approximation to an NP-complete optimization problem) might become worthwhile only because a solution can be applied many times for different problem instances.
Keywords
graph theory; statistical distributions; NP-complete optimization problem; dynamic graphical models; graph theory; probability distribution; Graphical models; Hidden Markov models; Inference algorithms; Junctions; Markov processes; Signal processing algorithms;
fLanguage
English
Journal_Title
Signal Processing Magazine, IEEE
Publisher
ieee
ISSN
1053-5888
Type
jour
DOI
10.1109/MSP.2010.938078
Filename
5563114
Link To Document