Fisher scoring and a mixture of modes approach for approximate inference and learning in nonlinear state space models

Advances in Neural Information Processing Systems

Volumn , Issue , 1999, Pages 403-409

  Briegel, Thomas ^a   Tresp, Volker ^a  

^a SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

MIXTURES; NONLINEAR EQUATIONS; STATE SPACE METHODS;

APPROXIMATE INFERENCE; FISHER SCORING ALGORITHM; GAUSSIAN APPROXIMATIONS; HIDDEN VARIABLE; INDEPENDENT SAMPLES; MULTIMODAL DISTRIBUTIONS; NONLINEAR STATE SPACE MODELS; POSTERIOR DISTRIBUTIONS;

APPROXIMATION ALGORITHMS;

EID: 3543070663     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. Anderson, B. and Moore, J. (1979) Optimal Filtering, Prentice-Hall, New Jersey
2. Fahrmeir, L. and Kaufmann, H. (1991) On Kalman Filtering, Posterior Mode Estimation and Fisher Scoring in Dynamic Exponential Family Regression, Metrika, 38, pp. 37-60.
3. Ghahramani, Z. and Roweis, S. (1999) Learning Nonlinear Stochastic Dynamics using the Generalized EM Algorithm, Advances in Neural Information Processing Systems 11, eds. M. Keams, S. Solla, D. Cohn, MIT Press, Cambridge, MA.
4. Kitagawa, G. (1987) Non-Gaussian State Space Modeling of Nonstationary Time Series (with Comments), JASA 82, pp. 1032-1063.
5. Puskorius, G. and Feldkamp, L. (1994) Neurocontrol of Nonlinear Dynamical Systems with Kalman Filter Trained Recurrent Networks, IEEE Transactions on Neural Networks, 5:2, pp. 279-297
6. Sage, A. and Melsa, J. (1971) Estimation Theory with Applications to Communications and Control, McGraw-Hill, New York.
7. Shumway, R. and Stoffer, D. (1982) Time Series Smoothing and Forecasting Using the EM Algorithm, Technical Report No. 27, Division of Statistics, UC Davis.
8. Tresp, V. and Hofmann, R. (1995) Missing and Noisy Data in Nonlinear Time-Series Prediction, Neural Networks for Signal Processing 5, IEEE Sig. Proc. Soc, pp. 1-10.