References
- I. Zeifman, 'General Birth-death processes and simple stochastic epidemic models,' Automation and Remote Control, vol. 46, no. 6, pp. 789-795, 1985
- S. Blaabjerg and H. Andersson, 'Approximating the heterogeneous fluid queue with a birth-death fluid queue,' IEEE Trans. on Communications, vol. 43, no. 5, pp. 1884-1887, 1995 https://doi.org/10.1109/26.387416
- M. Alonso and F. J. Alguacil, 'Stochastic modeling of particle coating.' AIChE Journal, vol. 47, no. 6, pp. 1303-1308, 2001 https://doi.org/10.1002/aic.690470608
- S. C. Kou, 'Modeling growth stocks via birth-death processes,' Advances in Applied Probability, vol. 35, no. 3, pp. 641-664, 2003 https://doi.org/10.1239/aap/1059486822
- P. R. Parthasarathy and K. V. Vijayashree, 'Fluid queues driven by birth and death processes with quadratic rates,' International Journal of Computer Mathematics, vol. 80, no. 11, pp. 1385-1395, 2003 https://doi.org/10.1080/0020716031000120836
- V. Rykov, 'Generalized birth-death processes and their application to the ageing models,' Automation and Remote Control, vol. 67, no. 3, pp. 435-451, 2006 https://doi.org/10.1134/S0005117906030088
- K. Murphy, 'Dynamic Bayesian networks: Representation, Inference and Learning.' Ph.D. Dissertation, UC Berkeley, 2002
- J. N. Daigle, Queuing theory with applications to packet telecommunication, New York, Springer, 2005
- J. M. Mendel, Lessons in estimation theory for signal processing, communications, and control, New Jersey, Prentice Hall, 1995
- W. J. Rugh, Linear system theory, Prentice Hall, 1996
- Y.-H. Wen, T-T Lee, and H-J Cho, 'Hybrid Greybased recurrent neural networks for short-term traffic forecasting and dynamic travel time estimation,' IEEE Conference on Intelligent Transportation Systems, 2005
Cited by
- Dynamic Bayesian modelling of non-stationary stochastic systems using constrained least square estimation and gradient descent optimisation vol.6, pp.6, 2012, https://doi.org/10.1049/iet-spr.2010.0081
- Fault Detection and Isolation of Induction Motors Using Recurrent Neural Networks and Dynamic Bayesian Modeling vol.18, pp.2, 2010, https://doi.org/10.1109/TCST.2009.2020863