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Evers, S. and Fokkinga, M.M. and Apers, P.M.G.
(2007)
Composable Markov Building Blocks.
In: Proceedings of the 1st International Conference on Scalable Uncertainty Management (SUM 2007), 10-12 Oct 2007, Washington DC, USA.
pp. 131-142.
Lecture Notes in Computer Science 4772.
Springer Verlag.
ISBN 978-3-540-75407-7
This is the latest version of this eprint. Full text available as:
Official URL: http://dx.doi.org/10.1007/978-3-540-75410-7_10  AbstractIn 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.
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