Change search
ReferencesLink to record
Permanent link

Direct link
EM versus Markov chain Monte Carlo for estimation of hidden Markov models: a computational perspective
Lund University.
2008 (English)In: Bayesian Analysis, ISSN 1931-6690, Vol. 3, no 4, 659-688 p.Article in journal (Refereed) Published
Abstract [en]

Hidden Markov models (HMMs) and related models have become standard in statistics during the last 15-20 years, with applications in diverse areas like speech and other statistical signal processing, hydrology, financial statistics and econometrics, bioinformatics etc. Inference in HMMs is traditionally often carried out using the EM algorithm, but examples of Bayesian estimation, in general implemented through Markov chain Monte Carlo (MCMC) sampling are also frequent in the HMM literature. The purpose of this paper is to compare the EM and MCMC approaches in three cases of different complexity; the examples include model order selection, continuous-time HMMs and variants of HMMs in which the observed data depends on many hidden variables in an overlapping fashion. All these examples in some way or another originate from real-data applications. Neither EM nor MCMC analysis of HMMs is a black-box methodology without need for user-interaction, and we will illustrate some of the problems, like poor mixing and long computation times, one may expect to encounter.

Place, publisher, year, edition, pages
2008. Vol. 3, no 4, 659-688 p.
Keyword [en]
hidden Markov model, incomplete data, missing data, EM, Markov chain Monte Carlo, trans-dimensional Monte Carlo, computational statistics
National Category
Probability Theory and Statistics
URN: urn:nbn:se:kth:diva-61183DOI: 10.1214/08-BA326ISI: 000207455100001OAI: diva2:478683
QC 20120117Available from: 2012-01-16 Created: 2012-01-16 Last updated: 2012-01-17Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Rydén, Tobias
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 50 hits
ReferencesLink to record
Permanent link

Direct link