Variational inference and learning for continuous-time nonlinear state-space models
published: Aug. 5, 2008, recorded: May 2008, views: 3347
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Inference in continuous-time stochastic dynamical models is a challenging problem. To complement existing sampling-based methods, variational methods have recently been developed for this problem. Our approach solves the variational continuous-time inference problem by discretisation that essentially reduces it to a discrete-time problem. Our framework makes learning the model in addition to inference easy. Other extensions such as heteroscedastic models are also relatively easy to consider within this framework.
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