Title :
Asymptotic smoothing errors for hidden Markov models
Author :
Shue, Louis ; Anderson, Brian D O ; De Bruyne, Franky
Author_Institution :
Centre for Signal Process., Nanyang Technol. Univ., Singapore
fDate :
12/1/2000 12:00:00 AM
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 ε, which is a measure of the frequency of change. Thus, when ε→0, the HMM becomes increasingly slower moving. We show that the smoothing error is O(ε). These theoretical predictions are confirmed by a series of simulations.
Keywords :
digital filters; error analysis; hidden Markov models; smoothing methods; HMM; asymptotic smoothing errors; filtering; frequency of change; hidden Markov models; hypothesis testing; positive constant; Filtering; Filters; Frequency measurement; Helium; Hidden Markov models; Predictive models; Smoothing methods; State estimation; Testing; Time measurement;
Journal_Title :
Signal Processing, IEEE Transactions on
