Abstract
Gaussian processes are attractive models for probabilistic classification but unfortunately exact inference is analytically intractable. We compare Laplace‘s method and Expectation Propagation (EP) focusing on marginal likelihood estimates and predictive performance. We explain theoretically and corroborate empirically that EP is superior to Laplace. We also compare to a sophisticated MCMC scheme and show that EP is surprisingly accurate.
Keywords
Laplace's methodGaussian processExpectation propagationInferenceLaplace transformMarginal likelihoodGaussianApplied mathematicsProbabilistic logicComputer scienceApproximate inferenceScheme (mathematics)AlgorithmArtificial intelligenceMarkov chain Monte CarloMachine learningBayesian inferenceBayesian probabilityMathematicsPhysicsMathematical analysis
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Publication Info
- Year
- 2005
- Type
- article
- Volume
- 18
- Pages
- 699-706
- Citations
- 25
- Access
- Closed
External Links
Citation Metrics
25
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Cite This
Malte Kuß,
Carl Edward Rasmussen
(2005).
Assessing Approximations for Gaussian Process Classification.
, 18
, 699-706.