Probabilistic Processing of Interval-valued Sensor Data

S. Evers, M.M. Fokkinga, Peter M.G. Apers

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When dealing with sensors with different time resolutions, it is desirable to model a sensor reading as pertaining to a time interval rather than a unit of time. We introduce two variants on the Hidden Markov Model in which this is possible: a reading extends over an arbitrary number of hidden states. We derive inference algorithms for the models, and analyse their efficiency. For this, we introduce a new method: we start with an inefficient algorithm directly derived from the model, and visually optimize it using a sum-factor diagram.
Original languageUndefined
Title of host publicationProceedings of the 5th International Workshop on Data Management for Sensor Networks (DMSN2008)
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages7
ISBN (Print)978-1-60558-284-9
Publication statusPublished - 24 Aug 2008

Publication series

NameACM International Conference Proceeding Series


  • IR-64883
  • EWI-13072
  • METIS-251087
  • DB-DMSN: Data Management for Sensor Networks
  • CR-H.2.8

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