2010
AISTATS
AISTATS 2010
State-Space Inference and Learning with Gaussian Processes
Abstract
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model.
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Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics
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Trend Setter
- Time Series Analysis
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Hot Topic Early Bird
- state space model
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Optimization & Theory > Stochastic Processes
Data Science & Analytics > Methods > Time Series Analysis
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Bayesian & Probabilistic > Gaussian Processes