Skew Gaussian processes for classification

DOI10.1007/s10994-020-05906-3OpenAlexW3083426028MaRDI QIDQ2217445

Alessio Benavoli, Dario Piga, Dario Filippo Azzimonti

Publication date: 29 December 2020

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.12987

zbMATH Keywords

classifiernonparametricprobitskewconjugateskew Gaussian process

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Scalable Computation of Predictive Probabilities in Probit Models with Gaussian Process Priors ⋮ Bayesian Conjugacy in Probit, Tobit, Multinomial Probit and Extensions: A Review and New Results ⋮ A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew Gaussian processes

Uses Software

• sn
• GPy
• GitHub
• PMLB
• QSIMVN

Cites Work

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• Fast computation of high-dimensional multivariate normal probabilities
• Computation of multivariate normal and \(t\) probabilities
• Bayes-Hermite quadrature
• Estimating Orthant Probabilities of High-Dimensional Gaussian Vectors with An Application to Set Estimation
• Hierarchical Decompositions for the Computation of High-Dimensional Multivariate Normal Probabilities
• On the Unification of Families of Skew-normal Distributions
• Bayes estimation subject to uncertainty about parameter constraints
• Conjugate Bayes for probit regression via unified skew-normal distributions
• The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting

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