Learning GP-BayesFilters via Gaussian process latent variable models

Jonathan Ko; Dieter Fox

doi:10.1007/s10514-010-9213-0

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Learning GP-BayesFilters via Gaussian process latent variable models

Jonathan Ko Dieter Fox

Year: 2010 Journal: Autonomous Robots Vol: 30 (1)Pages: 3-23 Publisher: Springer Science+Business Media

DOI: 10.1007/s10514-010-9213-0

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Keywords:

Computer science Ground truth Gaussian process Artificial intelligence Kalman filter Inertial measurement unit Latent variable Parametric statistics Machine learning Particle filter Bayesian probability Kriging Gaussian Mathematics

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Topics

Gaussian Processes and Bayesian Inference

Physical Sciences → Computer Science → Artificial Intelligence

Control Systems and Identification

Physical Sciences → Engineering → Control and Systems Engineering

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

Learning GP-BayesFilters via Gaussian process latent variable models

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