Team for Research in
Ubiquitous Secure Technology

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.

Electronic downloads

• siracusafisher_asilomar06.pdf · application/pdf · 157 kbytes

• 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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