Asymptotic Smoothing errors for Hidden Markov Models
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Shue, L
Anderson, Brian
De Bruyne, Franky
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Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
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.
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IEEE Transactions on Signal Processing