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Séminaires REPARTI

Les Séminaires CerVIM, Université Laval ont lieu le vendredi à 11h30.
Veuillez consulter le programme pour plus de détails.







Mar 1 2013 11:30AM

Yali Wang
Laboratoire DAMAS

A Marginalized Particle Gaussian Process Regression


We present a novel marginalized particle Gaussian process (MPGP) regression, which provides a fast, accurate online Bayesian filtering framework to model the latent function. Using a state space model established by the data construction procedure, our MPGP recursively filters out the estimation of hidden function values by a Gaussian mixture. Meanwhile, it provides a new online method for training hyperparameters with a number of weighted particles. We demonstrate the estimated performance of our MPGP on both simulated and real large data sets. The results show that our MPGP is a robust estimation algorithm with high computational efficiency, which outperforms other state-of-art sparse GP methods.


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