Inferring state sequences for non-linear systems with embedded hidden markov models

Advances in Neural Information Processing Systems

Volumn , Issue , 2004, Pages

  Neal, Radford M ^a   Beal, Matthew J ^a   Roweis, Sam T ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

FACE RECOGNITION; LINEAR CONTROL SYSTEMS; MARKOV PROCESSES;

DISCRETIZATION ERRORS; DYNAMIC PROGRAMMING ALGORITHM; EMBEDDED HIDDEN MARKOV MODEL; HIDDEN STATE; HUMAN MOTIONS; MARKOV CHAIN METHOD; NUMBER OF STATE; STATE SEQUENCES;

LAKES;

EID: 84898932109     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. Achan, K., Roweis, S. T., and Frey, B. J. (2004) "A Segmental HMM for Speech Waveforms", Technical Report UTML-TR-2004-001, University of Toronto, January 2004.
2. Doucet, A., Godsill, S. J., and West, M. (2000) "Monte Carlo filtering and smoothing with application to time-varying spectral estimation" Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2000, volume II, pages 701-704.
3. Neal, R. M. (1993) Probabilistic Inference Using Markov Chain Monte Carlo Methods, Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto, 144 pages. Available from http://www.cs.utoronto.ca/radford.
4. Neal, R. M. (2003) "Markov chain sampling for non-linear state space models using embedded hidden Markov models", Technical Report No. 0304, Dept. of Statistics, University of Toronto, 9 pages. Available from http://www.cs.utoronto.ca/radford.
5. Scott, S. L. (2002) "Bayesian methods for hidden Markov models: Recursive computing in the 21st century", Journal of the American Statistical Association, vol. 97, pp. 337-351.