About: Gaussian process approximations

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In statistics and machine learning, Gaussian process approximation is a computational method that accelerates inference tasks in the context of a Gaussian process model, most commonly likelihood evaluation and prediction. Like approximations of other models, they can often be expressed as additional assumptions imposed on the model, which do not correspond to any actual feature, but which retain its key properties while simplifying calculations. Many of these approximation methods can be expressed in purely linear algebraic or functional analytic terms as matrix or function approximations. Others are purely algorithmic and cannot easily be rephrased as a modification of a statistical model.

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rdfs:label	Gaussian process approximations (en)
rdfs:comment	In statistics and machine learning, Gaussian process approximation is a computational method that accelerates inference tasks in the context of a Gaussian process model, most commonly likelihood evaluation and prediction. Like approximations of other models, they can often be expressed as additional assumptions imposed on the model, which do not correspond to any actual feature, but which retain its key properties while simplifying calculations. Many of these approximation methods can be expressed in purely linear algebraic or functional analytic terms as matrix or function approximations. Others are purely algorithmic and cannot easily be rephrased as a modification of a statistical model. (en)
dct:subject	Computational statistics Geostatistics Computational science
Wikipage page ID	64322620 (xsd:integer)
Wikipage revision ID	1111840187 (xsd:integer)
Link from a Wikipage to another Wikipage	Determinant Computational statistics Computational science Geostatistics Gaussian process Likelihood Linear algebra Computational complexity Functional analysis Kriging Geostatistics Graphical model Probability density function Invertible matrix Covariance matrix Computational science Vecchia approximation Statistical modeling
sameAs	Gaussian process approximations Gaussian process approximations
dbp:wikiPageUsesTemplate	dbt:Cite_journal dbt:More_footnotes
has abstract	In statistics and machine learning, Gaussian process approximation is a computational method that accelerates inference tasks in the context of a Gaussian process model, most commonly likelihood evaluation and prediction. Like approximations of other models, they can often be expressed as additional assumptions imposed on the model, which do not correspond to any actual feature, but which retain its key properties while simplifying calculations. Many of these approximation methods can be expressed in purely linear algebraic or functional analytic terms as matrix or function approximations. Others are purely algorithmic and cannot easily be rephrased as a modification of a statistical model. (en)
prov:wasDerivedFrom	wikipedia-en:Gaussian_process_approximations?oldid=1111840187&ns=0
page length (characters) of wiki page	12708 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Gaussian_process_approximations
is rdfs:seeAlso of	Gaussian process
is Link from a Wikipage to another Wikipage of	Gaussian process Comparison of Gaussian process software Kriging Vecchia approximation
is foaf:primaryTopic of	wikipedia-en:Gaussian_process_approximations

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