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2008
ACM

Probabilistic processing of interval-valued sensor data

8 years 6 months ago
Probabilistic processing of interval-valued sensor data
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. Categories and Subject Descriptors H.2.8 [Database Applications]: Statistical databases-sensor data General Terms Algorithms, Theory, Performance Keywords Intervals, Probabilistic Inference, Hidden Markov Model
Sander Evers, Maarten M. Fokkinga, Peter M. G. Ape
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where DMSN
Authors Sander Evers, Maarten M. Fokkinga, Peter M. G. Apers
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