Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression

Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression

Show other versions (1)

This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched with various corresponding correlation structures of the obse...

Full description

Bibliographic Details
Main Authors: Hoang, Trong Nghia (Author), Jaillet, Patrick (Author)
Other Authors: Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2021-01-08T15:11:59Z.
Subjects:
Article
Online Access:Get fulltext

Internet

Get fulltext

Similar Items