Bayesian M-T clustering for reduced parameterisation of Markov chains used for non-linear adaptive elements

Automatica

Volumn 37, Issue 7, 2001, Pages 1071-1078

  Valečková, Markéta ^a   Kárný, Miroslav ^a   Sutanto, Elmawati L ^b  

^a INSTITUTE OF INFORMATION THEORY AND AUTOMATION   (Czech Republic)

^b UNIVERSITY OF READING   (United Kingdom)

Author keywords

Bayesian mixture estimation; Clustering; Markov chain

Indexed keywords

ALGORITHMS; MARKOV PROCESSES; NONLINEAR CONTROL SYSTEMS; PARAMETER ESTIMATION;

BAYESIAN MIXTURE ESTIMATION; MEAN TRACKING CLUSTERING ALGORITHM;

ADAPTIVE CONTROL SYSTEMS;

EID: 0035400456     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0005-1098(01)00060-7     Document Type: Article

References (21)

1. Identification of reality in Bayesian context
2. Model-based texture segmentation and classification
3. Performance of feasible Markov chain-based predictors for non-linear systems
4. Adaptive systems: Local approximators?
5. Quasi-Bayes estimation applied to normal mixtures
6. Quantification of prior information revised
7. On general concept of forgetting
8. Bayesian learning for neural networks
9. Bayesian system identification
10. On bayesian analysis of mixtures with an unknown number of components, with discussion
11. Multivariable cluster analysis for high-speed industrial machinery
12. On solving incorrectly posed problems and method of regularization
13. Estimation of Markov chains with reduced parameterisation