Asymptotic Smoothing errors for Hidden Markov Models
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Shue, L; Anderson, Brian; De Bruyne, Franky
Description
In this paper, the asymptotic smoothing error for hidden Markov models (HMMs) is investigated using hypothesis testing ideas. A family of HMMs is studied parametrised by a positive constant e, which is a measure of the frequency of change. Thus, when e -> 0, the HMM becomes increasingly slower moving. We show that the smoothing error is O(e). These theoretical predictions are confirmed by a series of simulations.
Collections | ANU Research Publications |
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Date published: | 2000 |
Type: | Journal article |
URI: | http://hdl.handle.net/1885/33693 |
Source: | IEEE Transactions on Signal Processing |
DOI: | 10.1109/78.886992 |
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