M. J. Johnson and A. S. Willskya
Stochastic variational inference for Bayesian time series models.
International Conference on Machine Learning, 2014
E. B. Fox, M. C. Hughes, E. B. Sudderth, and M. I. Jordan.
Joint modeling of multiple time series via the beta process with application to motion capture segmentation.
Annals of Applied Statistics, 8(3):1281–1313, 2014.
Classic references
An Introduction to Hidden Markov Models and Bayesian Networks
Zoubin Ghahramani
International Journal of Artificial Intelligence and Pattern Recognition, 2001
http://mlg.eng.cam.ac.uk/zoubin/papers/ijprai.pdf
D. J. C. MacKay.
Ensemble learning for hidden Markov models.
Technical report, Department of Physics, University of Cambridge, 1997.
L. R. Rabiner
A tutorial on hidden Markov models and selected applications in speech recognition.
Proc.of the IEEE, 77(2):257–286, 1989
Michael C. Hughes PhD Thesis Chapter on HMMs https://cs.brown.edu/research/pubs/theses/phd/2016/hughes.michael.pdf#page=155
M. J. Johnson and A. S. Willskya Stochastic variational inference for Bayesian time series models. International Conference on Machine Learning, 2014
E. B. Fox, M. C. Hughes, E. B. Sudderth, and M. I. Jordan. Joint modeling of multiple time series via the beta process with application to motion capture segmentation. Annals of Applied Statistics, 8(3):1281–1313, 2014.
Classic references
An Introduction to Hidden Markov Models and Bayesian Networks Zoubin Ghahramani International Journal of Artificial Intelligence and Pattern Recognition, 2001 http://mlg.eng.cam.ac.uk/zoubin/papers/ijprai.pdf
D. J. C. MacKay. Ensemble learning for hidden Markov models. Technical report, Department of Physics, University of Cambridge, 1997.
L. R. Rabiner A tutorial on hidden Markov models and selected applications in speech recognition. Proc.of the IEEE, 77(2):257–286, 1989