Nonlinear Filters

References

 

1. 1 R. E. Kalman, “On the general theory of control systems,” in Proceedings First International Conference on Automatic Control, Moscow, USSR, 1960, pp. 481–492.
2. 2 M. Karl, M. Soelch, J. Bayer, and P. van der Smagt, “Deep variational Bayes filters: Unsupervised learning of state space models from raw data,” in International Conference on Learning Representations, 2017, pp. 1–13.
3. 3 R. G. Brown and P. Y. Hwang, Introduction to Random Signals and Applied Kalman Filtering with MATLAB Exercises, 4th ed. John Wiley & Sons, New York, NY, USA, 2012.
4. 4 B. C. Smith, “The foundations of computing,” in Computationalism: New Directions, M. Scheutz, Ed. Cambridge, MA, USA: The MIT Press, 2002, Ch. 2, pp. 23–58.
5. 5 L. Breiman, “Statistical modeling: The two cultures (with comments and a rejoinder by the author),” Statistical Science, vol. 16, no. 3, pp. 199–231, 2001.
6. 6 A. Y. Ng and M. I. Jordan, “On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes,” in Advances in Neural Information Processing Systems, 2002, pp. 841–848.
7. 7 R. Shwartz‐Ziv and N. Tishby, “Opening the black box of deep neural networks via information,” arXiv preprint arXiv:1703.00810, 2017.
8. 8 K. P. Murphy, “Dynamic Bayesian networks: Representation, inference and learning,” Ph.D. dissertation, University of California, Berkeley, 2002.
9. 9 K. R. Muske and T. F. Edgar, “Nonlinear state estimation,” in Nonlinear Process Control, M. A. Henson and D. E. Seborg, Eds. Upper Saddle ...