87 search results - page 3 / 18 » Novel approximations for inference and learning in nonlinear... |
NIPS
1998
13 years 6 months ago
1998
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
JMLR
2006
13 years 5 months ago
2006
We introduce a method for approximate smoothed inference in a class of switching linear dynamical systems, based on a novel form of Gaussian Sum smoother. This class includes the ...
NIPS
2008
13 years 6 months ago
2008
Identification and comparison of nonlinear dynamical system models using noisy and sparse experimental data is a vital task in many fields, however current methods are computation...
ICML
2006
IEEE
14 years 6 months ago
2006
IEEE
The recent Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent...
ISIPTA
2003
IEEE
13 years 10 months ago
2003
IEEE
Set-valued estimation offers a way to account for imprecise knowledge of the prior distribution of a Bayesian statistical inference problem. The set-valued Kalman filter, which p...