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We present a novel approach for learning nonlinear dynamic models, which leads to a new set of tools capable of solving problems that are otherwise difficult.
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon et al.][1993]GSS93 Gordon, N. J., Salmond, D. J., & Smith, A. (1993) · 1993
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Bayesian forecasting and dynamic models (2nd ed.)
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Parametric hidden markov models for gesture recognition
Wilson and Bobick][1999]WB Wilson, A. D., & Bobick, A. F. (1999) · 1999
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Mixture Kalman filters
Chen and Liu][2000]ChenLiu00 Chen, R., & Liu, J. S. (2000) · 2000
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Roweis and Ghahramani][2001]RG Roweis, S., & Ghahramani, Z. (2001) · 2001
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Arulampalam et al.][2002]Arulampalam2002 Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002) · 2002
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Kakade and Langford][2002]CPI Kakade, S., & Langford, J. (2002) · 2002
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Taylor et al.][2006]conf/nips/Graham Taylor, G. W., Hinton, G. E., & Roweis, S. T. (2006) · 2006
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Hsu et al.][2008]Linear Hsu, D., Kakade, S. M., & Zhang, T. (2008) · 2008
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Daume et al.][2009]Searn Daume, H., Langford, J., & Marcu, D. (2009) · 2009
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