Maximum-likelihood estimation for hidden Markov models

Brian G. Leroux

Stochastic Processes and their Applications, 1992, vol. 40, issue 1, 127-143

Abstract: Hidden Markov models assume a sequence of random variables to be conditionally independent given a sequence of state variables which forms a Markov chain. Maximum-likelihood estimation for these models can be performed using the EM algorithm. In this paper the consistency of a sequence of maximum-likelihood estimators is proved. Also, the conclusion of the Shannon-McMillan-Breiman theorem on entropy convergence is established for hidden Markov models.

Keywords: Markov; chain; consistency; subadditive; ergodic; theorem; identifiability; entropy; Kullback-Leibler; divergence; Shannon-McMillan-Breiman; theorem (search for similar items in EconPapers)
Date: 1992
References: Add references at CitEc
Citations: View citations in EconPapers (68)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0304-4149(92)90141-C
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:spapps:v:40:y:1992:i:1:p:127-143

Ordering information: This journal article can be ordered from
http://http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Stochastic Processes and their Applications is currently edited by T. Mikosch

More articles in Stochastic Processes and their Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().