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Inferring Dynamic Dependency with Applications to Link Analysis
Michael Siracusa, John Fisher

Citation
Michael Siracusa, John Fisher. "Inferring Dynamic Dependency with Applications to Link Analysis". Proceeding of the 40th Asilomar Conference On Signals, Systems, and Computers, November, 2006.

Abstract
Statistical approaches to modeling dynamics and clustering data are well studied research areas. This paper considers a special class of such problems in which one is presented with multiple data streams and wishes to infer their interaction as it evolves over time. This problem is viewed as one of inference on a class of models in which interaction is described by changing dependency structures, i.e. the presence or absence of edges in a graphical model, but for which the full set of parameters are not available. The application domain of dynamic link analysis as applied to tracked object behavior is explored. An approximate inference method is presented along with empirical results demonstrating its performance.

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Citation formats  
  • HTML
    Michael Siracusa, John Fisher. <a
    href="http://www.truststc.org/pubs/273.html"
    >Inferring Dynamic Dependency with Applications to Link
    Analysis</a>, Proceeding of the 40th Asilomar
    Conference On Signals, Systems, and Computers, November,
    2006.
  • Plain text
    Michael Siracusa, John Fisher. "Inferring Dynamic
    Dependency with Applications to Link Analysis".
    Proceeding of the 40th Asilomar Conference On Signals,
    Systems, and Computers, November, 2006.
  • BibTeX
    @inproceedings{SiracusaFisher06_InferringDynamicDependencyWithApplicationsToLinkAnalysis,
        author = {Michael Siracusa and John Fisher},
        title = {Inferring Dynamic Dependency with Applications to
                  Link Analysis},
        booktitle = {Proceeding of the 40th Asilomar Conference On
                  Signals, Systems, and Computers},
        month = {November},
        year = {2006},
        abstract = {Statistical approaches to modeling dynamics and
                  clustering data are well studied research areas.
                  This paper considers a special class of such
                  problems in which one is presented with multiple
                  data streams and wishes to infer their interaction
                  as it evolves over time. This problem is viewed as
                  one of inference on a class of models in which
                  interaction is described by changing dependency
                  structures, i.e. the presence or absence of edges
                  in a graphical model, but for which the full set
                  of parameters are not available. The application
                  domain of dynamic link analysis as applied to
                  tracked object behavior is explored. An
                  approximate inference method is presented along
                  with empirical results demonstrating its
                  performance.},
        URL = {http://www.truststc.org/pubs/273.html}
    }
    

Posted by Michael Siracusa on 24 Jul 2007.
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