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Asymptotic Smoothing errors for Hidden Markov Models

Shue, L; Anderson, Brian; De Bruyne, Franky


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.

CollectionsANU Research Publications
Date published: 2000
Type: Journal article
Source: IEEE Transactions on Signal Processing
DOI: 10.1109/78.886992


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