Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Distributed Parameter Systems: Identification, Estimation and Control
Distributed Parameter Systems: Identification, Estimation and Control
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models
Neural Information Processing
Approximate Marginals in Latent Gaussian Models
The Journal of Machine Learning Research
Expectation propagation for approximate inference in dynamic bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
In this paper, we consider learning of spatio-temporal processes by formulating a Gaussian process model as a solution to an evolution type stochastic partial differential equation. Our approach is based on converting the stochastic infinite-dimensional differential equation into a finite dimensional linear time invariant (LTI) stochastic differential equation (SDE) by discretizing the process spatially. The LTI SDE is time-discretized analytically, resulting in a state space model with linear-Gaussian dynamics. We use expectation propagation to perform approximate inference on non-Gaussian data, and show how to incorporate sparse approximations to further reduce the computational complexity. We briefly illustrate the proposed methodology with a simulation study and with a real world modelling problem.