Nonparametric Belief Propagation.
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 prov...
| Publicado en: | Communications of the ACM Vol. 53; no. 10; pp. 95 - 104 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Association for Computing Machinery
Oct2010
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=55028311&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 55028311 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Oct2010 vid: 53 iid: 10 pid: 68 pub: Association for Computing Machinery artinfo: ui: 55028311 10.1145/1831407.1831431 ppf: 95 ppct: 9 formats: tig: atl: Nonparametric Belief Propagation. aug: au: Sudderth, Erik B. Ihler, Alexander T. Isard, Michael Freeman, William T. Willsky, Alan S. affil: Brown University, Providence, RI. University of California, Irvine. Microsoft Research, Mountain View, CA. Massachusetts Institute of Technology, Cambridge, MA. su: Information modeling Nonparametric statistics Kinematics Localization theory Sensor networks Probability theory Graphical modeling (Statistics) sug: subj: Information modeling Nonparametric statistics Kinematics Localization theory Sensor networks Probability theory Graphical modeling (Statistics) ab: 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. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2010 holdings: @attributes: islocal: N |
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