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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Conference Pioneer
— AISTATS 2010
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Interdisciplinary Bridge
— Artificial Intelligence and Data Science & Analytics and Machine Learning
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Trend Setter
— Time Series Analysis
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Hot Topic Early Bird
— expectation maximization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Stochastic Processes
Data Science & Analytics > Methods > Time Series Analysis
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Bayesian & Probabilistic > Gaussian Processes