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Author Sudderth, Erik B. ♦ Freeman, William T. ♦ Willsky, Alan S. ♦ Isard, Michael ♦ Ihler, Alexander T.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Abstract Continuous quantities are ubiquitous in models of real-world phenomena, but are surprisingly difficult to reason about automatically. Probabilistic graphical models such as Bayesian networks and Markov random fields, and algorithms for approximate inference such as belief propagation (BP), have proven to be powerful tools in a wide range of applications in statistics and artificial intelligence. However, applying these methods to models with continuous variables remains a challenging task. In this work we describe an extension of BP to continuous variable models, generalizing particle filtering, and Gaussian mixture filtering techniques for time series to more complex models. We illustrate the power of the resulting nonparametric BP algorithm via two applications: kinematic tracking of visual motion and distributed localization in sensor networks.
Description Affiliation: Massachusetts Institute of Technology, Cambridge, MA (Freeman, William T.; Willsky, Alan S.) || Brown University, Providence, RI (Sudderth, Erik B.) || University of California, Irvine (Ihler, Alexander T.) || Microsoft Research, Mountain View, CA (Isard, Michael)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 53
Issue Number 10
Page Count 9
Starting Page 95
Ending Page 103


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Source: ACM Digital Library