B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization , and Beyond, 2001.

C. M. Bishop, Neural Networks for Pattern Recognition, 1995.

V. N. Vapnik, Statisical Learning Theory, 1998.

C. M. Bishop and M. E. Tipping, Bayesian Regression and Classification, " in Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, pp.267-285, 2003.

J. Vermaak, S. J. Godsill, and A. Doucet, Sequential Bayesian Kernel Regression, Advances in Neural Information Processing Systems 16, 2003.

Y. Engel, S. Mannor, and R. Meir, The Kernel Recursive Least-Squares Algorithm, IEEE Transactions on Signal Processing, vol.52, issue.8, pp.2275-2285, 2004.
DOI : 10.1109/TSP.2004.830985

E. A. Wan and R. Van-der-merwe, The unscented Kalman filter for nonlinear estimation, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373), pp.153-158, 2000.
DOI : 10.1109/ASSPCC.2000.882463

R. Van-der-merwe and E. A. Wan, Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models, Workshop on Advances in Machine Learning, 2003.

R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, pp.35-45, 1960.
DOI : 10.1115/1.3662552

Z. Chen, Bayesian Filtering : From Kalman Filters to Particle Filters, and Beyond, Tech. Rep, 2003.

M. Geist, O. Pietquin, and G. Fricout, Bayesian Reward Filtering, European Workshop on Reinforcement Learning (EWRL 2008), 2008.
DOI : 10.1007/978-3-540-89722-4_8
URL : https://hal.archives-ouvertes.fr/hal-00351282