Composable Markov Building Blocks

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


In situations where disjunct parts of the same process are described by their own first-order Markov models and only one model applies at a time (activity in one model coincides with non-activity in the other models), these models can be joined together into one. Under certain conditions, nearly all the information to do this is already present in the component models, and the transition probabilities for the joint model can be derived in a purely analytic fashion. This composability provides a theoretical basis for building scalable and flexible models for sensor data.
Original languageUndefined
Title of host publicationProceedings of the 1st International Conference on Scalable Uncertainty Management (SUM 2007)
EditorsH. Prade, V.S. Subrahmanian
Place of PublicationBerlin
Number of pages12
ISBN (Print)978-3-540-75407-7
Publication statusPublished - 10 Oct 2007
Event1st International Conference on Scalable Uncertainty Management 2007 - Washington, United States
Duration: 10 Oct 200712 Oct 2007
Conference number: 1

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag


Conference1st International Conference on Scalable Uncertainty Management 2007
Abbreviated titleSUM 2007
CountryUnited States


  • METIS-241794
  • IR-61856
  • Markov models
  • Sensor data management
  • EWI-10794

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