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Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems
Proceedings of the 23rd international conference on Machine learning - ICML '06
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 parameter estimation possible without any loss of modeling power and while using fewer parameters. In this paper we extend the PLG to model stochastic, nonlinear dynamical systems by using kernel methods. With a Gaussian kernel, the model admits closed form solutions to the state update equations due to conjugacydoi:10.1145/1143844.1143972 dblp:conf/icml/WingateS06 fatcat:oj6sawdetjhxxobqjtebny7jni