| S. Roweis and Z. Ghahramani, "An EM algorithm for identification of nonlinear dynamical systems," in Kalman Filtering and Neural Networks (S. Haykin, ed.). To appear. |
....observation model. 8 XAVIER GIANNAKOPOULOS AND HARRI VALPOLA 4. Dynamical extension of nonlinear factor analysis The problem of estimating a nonlinear dynamical factor analysis model has been considered easier than the problem of estimating a nonlinear factor analysis model without dynamics [11]. Given the success of the nonlinear factor analysis algorithm proposed in [1] to estimate factors of dimensionality up to ten, it is natural to try to extend the algorithm to take into account the dynamics of the factors. For instance the pulp process data presented in [1] was a time series which ....
.... gradients with respect to the posterior mean and variance of the factors, the nonlinear mapping g introduces simply an extra additive term which propagates the information from s(t) to s(t Gamma 1) It would be possible to use Kalman smoothing for updating the factor distributions as was done in [12,11]. This would mean that on each iteration, a forward and backward recursion of the factor distributions would be computed. On each iteration, the distribution of s(t) would be updated based on all the observations because the forward and backward recursion pass the information. However, we update ....
S. T. Roweis and Z. Ghahramani, "An EM algorithm for identification of nonlinear dynamical systems," in Kalman Filtering and Neural Networks, S. Haykin, ed. To appear.
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S. Roweis and Z. Ghahramani, "An EM algorithm for identification of nonlinear dynamical systems," in Kalman Filtering and Neural Networks (S. Haykin, ed.). To appear.
No context found.
S.Roweis and Z.Ghahramani, "An EM algorithm for identification of nonlinear dynamical systems," available from http://www.gatsby.ucl.ac.uk/~roweis/publications.html (2000).
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