Semi-hidden Markov models for generation and analysis of sequences

R. Román-Gálvez, R. Román-Roldán, J. Martínez-Aroza and J.F. Gómez-Lopera

Mathematics and Computers in Simulation (MATCOM), 2015, vol. 118, issue C, 320-328

Abstract: In this work a new kind of stochastic model is presented, the semi-hidden Markov model (SHMM). The proposed model is related to the hidden Markov model (HMM), and it is called semi-hidden because generated sequences need less information than HMM sequences to infer the succession of states run by the source.

Keywords: Hidden Markov models, HMM; Generation of symbolic sequences; Symbolic run sequences (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475414003085
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:matcom:v:118:y:2015:i:c:p:320-328

DOI: 10.1016/j.matcom.2014.11.009

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().