Treffer: An online sequential algorithm for the estimation of transition probabilities for jump Markov linear systems

Title:
An online sequential algorithm for the estimation of transition probabilities for jump Markov linear systems
Authors:
ORGUNER, Umut, DEMIREKLER, Mübeccel
Source:
Automatica (Oxford). 42(10):1735-1744
Publisher Information:
Oxford: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 26 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Automatique théorique. Systèmes, Control theory. Systems, Modélisation et identification, Modelling and identification, Approche déterministe, Deterministic approach, Enfoque determinista, Approximation stochastique, Stochastic approximation, Aproximación estocástica, Distribution aléatoire, Random distribution, Distribución aleatoria, Donnée manquante, Missing data, Dato que falta, Entropie, Entropy, Entropía, Estimation séquentielle, Sequential estimation, Estimación secuencial, Fonction vraisemblance, Likelihood function, Función verosimilitud, Loi a priori, Prior distribution, Ley a priori, Mesure distance, Distance measurement, Modèle Markov caché, Hidden Markov model, Modelo Markov oculto, Probabilité transition, Transition probability, Probabilidad transición, Processus Markov, Markov process, Proceso Markov, Processus saut, Jump process, Proceso salto, Variable aléatoire, Random variable, Variable aléatoria, Jump Markov linear systems, Kullback-Leibler distance measure
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of 'Electrical and Electronics Engineering, Middle East Technical University, 06531 Ankara, Turkey
ISSN:
0005-1098
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18067889
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18067889
Database:
PASCAL Archive

Weitere Informationen

This paper describes a new method to estimate the transition probabilities associated with a jump Markov linear system. The new algorithm uses stochastic approximation type recursions to minimize the Kullback-Leibler divergence between the likelihood function of the transition probabilities and the true likelihood function. Since the calculation of the likelihood function of the transition probabilities is impossible, an incomplete data paradigm, which has been previously applied to a similar problem for hidden Markov models, is used. The algorithm differs from the existing algorithms in that it assumes that the transition probabilities are deterministic quantities whereas the existing approaches consider them to be random variables with prior distributions.