References
- K. Murphy, 'Dynamic Bayesian networks: Representation, Inference and Learning.' Ph. D. Dissertation, UC Berkeley, 2002
- J. M. Mendel, Lessons in estimation theory for signal processing, communications, and control, Prentice Hall, 1995
- T. K. Moon, 'The expectation-maximization algorithm,' IEEE Signal Processing Magazine, vol. 13, no. 6, pp. 47-60, 1996 https://doi.org/10.1109/79.543975
- X. Huang, A. Acero, and H.-W. Hon, Spoken language processing, Prentice Hall, 2001
- J. Ying, T. Kirubarajan, K. R. Pattipati, A. Patterson-Hine, 'A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests,' IEEE Trans. on Systems, Man and Cybernetics, vol. 30, no. 4, pp. 463-473, 2000 https://doi.org/10.1109/5326.897073
- D. Hernandez-Hernandez, S. I. Marcus, and P. J. Fard, 'Analysis of a risk-sensitive control problem for hidden Markov chains,' IEEE Trans. on Automatic Control, vol. 44, no. 5, pp. 1093-1100, 1999 https://doi.org/10.1109/9.763237
- H. C. Cho, S. M. Fadali, and K. S. Lee, 'Estimation of non-Gaussian probability density by dynamic Bayesian networks,' Int. Conf on Control, Automation, and Systems, pp. 408-413, 2005
- P. Baldi and Y. Chauvin, 'Smooth on-line learning algorithm for hidden Markov models,' Neural Computation, vol. 6, no. 2, pp. 307-318, 1994 https://doi.org/10.1162/neco.1994.6.2.307
- J. J. Ford and J. B. Moore, 'Adaptive estimation of HMM transition probabilities,' IEEE Trans. on Signal Processing, vol. 46, no. 5, pp. 1374-1385, 1998 https://doi.org/10.1109/78.668799
- J. C. Stiller and G. Radons, 'Online estimation of hidden Markov models,' IEEE Signal Processing Letters, vol. 6, no. 8,pp.213-215, 1999 https://doi.org/10.1109/97.774870
- I. Cohen and A. Bronstein, 'Adaptive online learning of Bayesian network parameters,' http://www.hpl.hp.com /techreports /2001/HPL-2001-156.pdf, 2001
- S.-Z. Zhang, H. Yu, N-H. Yang, and X.-K Wang, 'An application of online learning algorithm for Bayesian network parameter,' Proc. of the Second International Conf. on Machine Learning and Cybernetics, pp. 153-156, 2003
- E. Parzen, 'On estimation of a probability density function and mode,' Annals of Mathematical Statistics, vol. 33, no. 3, pp. 1065-1076, 1962 https://doi.org/10.1214/aoms/1177704472
- B. W. Silverman, Density Estimation for Statistics and Data Analysis, Chapman & Hall/CRC, 1986
- V. Krishnamurthy, J. B. Moore, and S.-H. Chung, 'Hidden Markov model signal processing in presence of unknown deterministic inferences,' IEEE Transaction on Automatic Control, vol. 38, no. 1, pp. 146-152,1993 https://doi.org/10.1109/9.186328
- R. J. Serfling, Approximation theorems of mathematical statistics, Wiley, 1980
- W. J. Rugh, Linear system theory, Prentice Hall, 1996. 2004
Cited by
- Probabilistic Modeling of Photovoltaic Power Systems with Big Learning Data Sets vol.23, pp.5, 2013, https://doi.org/10.5391/JKIIS.2013.23.5.412