Gaussian Processes and Bayesian Inference
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Scope of this subject
This cluster of papers focuses on the application of Gaussian Processes in machine learning, covering topics such as variational inference, sparse regression, Bayesian inference, deep learning, and probabilistic models. It also explores the use of Gaussian Processes for nonparametric methods, time series modelling, and handling big data.
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Research vocabulary
Gaussian process priors; deep Gaussian processes; sparse Gaussian processes; Gaussian process emulation; scalable inference; expectation propagation; Gaussian process classification; inducing points; multi-output Gaussian processes; evidence lower bound; Gaussian process latent variable model; variational lower bound; Matérn kernel; stochastic variational inference; non-stationary Gaussian processes; scalable Bayesian inference; sparse Gaussian process regression; Bayesian nonparametric regression; Gaussian process approximation; hyperparameter learning; non-Gaussian likelihood; Vecchia approximation; computer model calibration; scalable Gaussian processes; computer model emulation
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No owner-reviewed answer has been published for this exact subject page. This is a gap in this collection; it does not establish that the answer is unavailable elsewhere.
Numeric address and provenance
Morton address: 38285820590984403 · morton4x16-v1
Coordinates: (3, 17, 1702, 12814). The address interleaves categorical identifier bits. Nearby addresses are not guaranteed to describe semantically similar subjects.
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