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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-king-ppa05, title = {Variational Inference in Gaussian Processes via Probabilistic Point Assimilation}, author = {Nathaniel J. King and Neil D. Lawrence}, year = {}, editor = {}, number = {CS-05-06}, url = {http://inverseprobability.com/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 Conference Paper %T Variational Inference in Gaussian Processes via Probabilistic Point Assimilation %A Nathaniel J. King %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-king-ppa05 %I PMLR %J Proceedings of Machine Learning Research %P -- %U http://inverseprobability.com %V %N CS-05-06 %W PMLR %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 - CPAPER TI - Variational Inference in Gaussian Processes via Probabilistic Point Assimilation AU - Nathaniel J. King AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-king-ppa05 PB - PMLR SP - DP - PMLR EP - L1 - UR - http://inverseprobability.com/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.. (). Variational Inference in Gaussian Processes via Probabilistic Point Assimilation. , in PMLR (CS-05-06):-

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