A Variational inference method for Switching Linear Dynamic Systems
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[1] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[2] L. Rabiner,et al. An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.
[3] R. Shumway,et al. Dynamic linear models with switching , 1991 .
[4] Mari Ostendorf,et al. A Dynamical System Approach to Continuous Speech Recognition , 1991, HLT.
[5] Yaakov Bar-Shalom,et al. Estimation and Tracking: Principles, Techniques, and Software , 1993 .
[6] J. R. Rohlicek,et al. ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition , 1993, IEEE Trans. Speech Audio Process..
[7] Chang‐Jin Kim,et al. Dynamic linear models with Markov-switching , 1994 .
[8] Mari Ostendorf,et al. From HMM's to segment models: a unified view of stochastic modeling for speech recognition , 1996, IEEE Trans. Speech Audio Process..
[9] Christoph Bregler,et al. Learning and recognizing human dynamics in video sequences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[10] Vladimir Pavlovic,et al. A dynamic Bayesian network approach to figure tracking using learned dynamic models , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[11] Zoubin Ghahramani,et al. A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.
[12] Gautam Biswas,et al. Bayesian Fault Detection and Diagnosis in Dynamic Systems , 2000, AAAI/IAAI.
[13] Vladimir Pavlovic,et al. Learning Switching Linear Models of Human Motion , 2000, NIPS.
[14] Vladimir Pavlovic,et al. Impact of dynamic model learning on classification of human motion , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[15] Michael Isard,et al. Learning and Classification of Complex Dynamics , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[16] Christophe Andrieu,et al. Iterative algorithms for state estimation of jump Markov linear systems , 2001, IEEE Trans. Signal Process..
[17] Arnaud Doucet,et al. Particle filters for state estimation of jump Markov linear systems , 2001, IEEE Trans. Signal Process..
[18] Terrence J. Sejnowski,et al. Variational Learning for Switching State-Space Models , 2001 .
[19] Uri Lerner,et al. Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms , 2001, UAI.
[20] Harry Shum,et al. Motion texture: a two-level statistical model for character motion synthesis , 2002, ACM Trans. Graph..
[21] Tom Heskes,et al. Hierarchical Visualization of Time-Series Data Using Switching Linear Dynamical Systems , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[22] Mark J. F. Gales,et al. Rao-Blackwellised Gibbs sampling for switching linear dynamical systems , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[23] James M. Rehg,et al. Data-Driven MCMC for Learning and Inference in Switching Linear Dynamic Systems , 2005, AAAI.