Learning interaction dynamics with coupled hidden Markov models

IEE Proceedings: Science, Measurement and Technology

Volumn 147, Issue 6, 2000, Pages 345-350

  Rezek, I ^a   Sykacek, P ^a   Roberts, S J ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BIOMEDICAL ENGINEERING; COMPUTER SIMULATION; MARKOV PROCESSES; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; STATE SPACE METHODS;

COUPLED HIDDEN MARKOV MODELS (CHMM);

SIGNAL PROCESSING;

EID: 0034313933     PISSN: 13502344     EISSN: None     Source Type: Journal    
DOI: 10.1049/ip-smt:20000851     Document Type: Article

References (19)

1. BANNISTER, R., and MATHIAS, C.J.: 'Autonomie failure, a textbook of clinical disorders of the autonomie nervous system' (Oxford University Press, Oxford, 1993, 3rd edn.)
2. KUSHWAHA, R.K., WILLIAMS, W.J., and SHEVRIN, H.: 'An information flow technique for category related evoked potentials', IEEE Tram. Biomcd. Ens;., 1992, 39, (2), pp. 165-175
3. AZZOUZI, M., and NABNEY, IT.: Time delay estimation with hidden Markov models'. Procedinos of ninth international conference on Artificial neural networks, 1999, (IEE), pp. 473-478
4. PENNY, W.D., and ROBERTS, S.J.: 'Gaussian observation hidden Markov models for EEG analysis'. Technical Report TR-9S-12, Imperial College London, 1998. Available from http://www.robots.ox.ac.uk/ sjrob/parg.html
5. BRAND, M.: 'Coupled hidden Markov models for modeling interacting processes'. Technical Report 405, MIT Media Lab Perceptual Computing, June 1997
6. ROWES, S., and GHAHRAMANI, Z.: 'A unifying review of linear Gaussian models', Neural Compiit., 1999, 11, (2), pp. 305-345
7. RAB1NER, L.R.: 'A tutorial on hidden Markov models and selected applications in speech recognition, Proc, IEEE, 1989, 77, (2), pp. 257284
8. FREY, B.J.: 'Graphical models for machine learning and dirital communication' (MIT Press, 1998)
9. PEARL, J.: 'Probabilistic reasoning in intelligent systems: networks of plausible inference' (Morgan Kaufmann, San Mateo, 1988, 2nd edn.)
10. LEE, P.M.: 'Bayesian statistics: an introduction' (Edward Arnold, 1989)
11. GILKS, W.R., THOMAS, A., and SPIEGELHALTER, D.J.: 'A languace and proeram for complex Bayesian modelling', Statistician, 1994,43, pp. 169-178
12. GHAHRAMANI, Z.: 'Learning dynamic Baycsian networks' m GILES, C.L., and GORI, M. (Eds.): 'Adaptive processing of sequences and data structures' (Sprincer-Verlac, Berlin, 1998), pp. 168-197
13. REZEK, I., and ROBERTS, S.J.: 'Stochastic complexity measures for physiological sienal analysis', IEEE Trims. Bionml. Eng., 1998,44, (9), pp. 11861191 -
14. SYKACEK, P.: 'Bayesian inference for reliable biomédical signal processing'. PhD thesis, Institute for Medical Cybernetics and Artificial Intelligence, 2000
15. ROBERTS, S.J., and TARASSENKO, L.: 'New method of automated sleep quantification', Mal. Biol. Eng. Compiit., 1992a, 30, (5), pp. 509-517
16. GERSCH, W., YONEMOTO, J., and NAITOH, P.: 'Automatic classification of multivariate EEGs using an amount of information measure and the eigenvalues of parametric time series model features', Comput. Biomed. Res., 1977, 10, pp. 297-318
17. REZEK, L, SYKACEK, P., and ROBERTS, S.J.: 'Coupled hidden Markov models for biosignal interaction modelling'. Technical Report PARG-00-05. Available from http://uvw.robots.o..ac.uk/-irezek, 2000
18. BRAND, M.: 'An entropie estimator for structure discovery' in 'Neural information processing systems, NIPS 11" (1999)
19. GREEN, P.: 'Reversible jump Markov chain Monte Carlo computation and Bayesian model determination', Biometrika, 1995, 82, pp. 711-732