By Topic

Complexity reduction in fixed-lag smoothing for hidden Markov models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Shue, L. ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore ; Dey, S.

We investigate approximate smoothing schemes for a class of hidden Markov models (HMMs), namely, HMMs with underlying Markov chains that are nearly completely decomposable. The objective is to obtain substantial computational savings. Our algorithm can not only be used to obtain aggregate smoothed estimates but can be used also to obtain systematically approximate full-order smoothed estimates with computational savings and rigorous performance guarantees, unlike many of the aggregation methods proposed earlier

Published in:

Signal Processing, IEEE Transactions on  (Volume:50 ,  Issue: 5 )