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.
|Name||ACM International Conference Proceeding Series|
|Workshop||5th International Workshop on Data Management for Sensor Networks, DMSN|
|Period||24/08/08 → 24/08/08|
|Other||24 Aug 2008|
- DB-DMSN: Data Management for Sensor Networks