Adaptive Dynamic Bayesian Networks

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A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can ... continued below

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PDF-file: 10 pages; size: 0.3 Mbytes

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Ng, B M October 26, 2007.

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A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can change from one time step to the next: a distribution's parameters and its set of conditional variables are dynamic. This work builds on recent work in nonparametric Bayesian modeling, such as hierarchical Dirichlet processes, infinite-state hidden Markov networks and structured priors for Bayes net learning. In this paper, we will explain the motivation for our interest in adaptive DBNs, show how popular nonparametric methods are combined to formulate the foundations for adaptive DBNs, and present preliminary results.

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PDF-file: 10 pages; size: 0.3 Mbytes

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  • Presented at: 2007 Joint Statistical Meetings, Salt Lake City, UT, United States, Jul 29 - Aug 02, 2007

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  • Report No.: UCRL-PROC-235983
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 919620
  • Archival Resource Key: ark:/67531/metadc900254

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  • October 26, 2007

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  • Sept. 27, 2016, 1:39 a.m.

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  • Dec. 6, 2016, 6:46 p.m.

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Ng, B M. Adaptive Dynamic Bayesian Networks, article, October 26, 2007; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc900254/: accessed September 20, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.