Recursive algorithms for estimation of hidden Markov models and autoregressive models with Markov regime

TitleRecursive algorithms for estimation of hidden Markov models and autoregressive models with Markov regime
Publication TypeJournal Article
Year of Publication2002
AuthorsKrishnamurthy, V., and G. G. Yin
JournalInformation Theory, IEEE Transactions on
Volume48
Pagination458 -476
Date Publishedfeb.
ISSN0018-9448
KeywordsAR models, autoregressive models, autoregressive processes, convergence, convergence of numerical methods, hidden Markov models, HMMs, iterates, iterative methods, Markov regime, maximum likelihood estimation, recursive algorithms, recursive estimation, tracking, tracking algorithms
Abstract

This paper is concerned with recursive algorithms for the estimation of hidden Markov models (HMMs) and autoregressive (AR) models under the Markov regime. Convergence and rate of convergence results are derived. Acceleration of convergence by averaging of the iterates and the observations are treated. Finally, constant step-size tracking algorithms are presented and examined
URLhttp://dx.doi.org/10.1109/18.979322
DOI10.1109/18.979322

a place of mind, The University of British Columbia

Electrical and Computer Engineering
2332 Main Mall
Vancouver, BC Canada V6T 1Z4
Tel +1.604.822.2872
Fax +1.604.822.5949
Email:

Emergency Procedures | Accessibility | Contact UBC | © Copyright 2021 The University of British Columbia