Variational Inference in <span>G</span>aussian Processes via Probabilistic Point Assimilation

Nathaniel J. King; Neil D. Lawrence

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Variational Inference in Gaussian Processes via Probabilistic Point Assimilation

Nathaniel J. King, Neil D. Lawrence

, 2005.

Abstract

We introduce a novel variational approach for approximate inference in Gaussian process (GP) models. The key advantages of our approach are the ease with which different noise models can be incorporated and improved speed of convergence. We refer to the algorithm as probabilistic point assimilation (PPA). We introduce the algorithm firstly using the ‘weight space’ view and then through its Gaussian process formulation. We illustrate the approach on several benchmark data sets.

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BibTeX


@Misc{King:ppa05,
  title = 	 {Variational Inference in Gaussian Processes via Probabilistic Point Assimilation},
  author = 	 {King, Nathaniel J. and Lawrence, Neil D.},
  year = 	 {2005},
  number =       {CS-05-06},
  url = 	 {/publications/king-ppa05.html},
  abstract = 	 {We introduce a novel variational approach for approximate inference in Gaussian process (GP) models. The key advantages of our approach are the ease with which different noise models can be incorporated and improved speed of convergence. We refer to the algorithm as probabilistic point assimilation (PPA). We introduce the algorithm firstly using the ‘weight space’ view and then through its Gaussian process formulation. We illustrate the approach on several benchmark data sets.}
}

Endnote

%0 Generic
%T Variational Inference in Gaussian Processes via Probabilistic Point Assimilation
%A Nathaniel J. King
%A Neil D. Lawrence
%D 2005	
%F King:ppa05
%U /publications/king-ppa05.html
%N CS-05-06
%X We introduce a novel variational approach for approximate inference in Gaussian process (GP) models. The key advantages of our approach are the ease with which different noise models can be incorporated and improved speed of convergence. We refer to the algorithm as probabilistic point assimilation (PPA). We introduce the algorithm firstly using the ‘weight space’ view and then through its Gaussian process formulation. We illustrate the approach on several benchmark data sets.

RIS


TY  - GEN
TI  - Variational Inference in Gaussian Processes via Probabilistic Point Assimilation
AU  - Nathaniel J. King
AU  - Neil D. Lawrence
DA  - 2005/01/01	
ID  - King:ppa05
IS  - CS-05-06
UR  - /publications/king-ppa05.html
AB  - We introduce a novel variational approach for approximate inference in Gaussian process (GP) models. The key advantages of our approach are the ease with which different noise models can be incorporated and improved speed of convergence. We refer to the algorithm as probabilistic point assimilation (PPA). We introduce the algorithm firstly using the ‘weight space’ view and then through its Gaussian process formulation. We illustrate the approach on several benchmark data sets.
ER  -

APA


King, N.J. & Lawrence, N.D.. (2005). Variational Inference in Gaussian Processes via Probabilistic Point Assimilation. (CS-05-06) Available from /publications/king-ppa05.html.