Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression

1 Nov 2017Haibin YuTrong Nghia HoangKian Hsiang LowPatrick Jaillet

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 observation noises. Our variational Bayesian SGPR (VBSGPR) models jointly treat both the distributions of the inducing variables and hyperparameters as variational parameters, which enables the decomposability of the variational lower bound that in turn can be exploited for stochastic optimization... (read more)

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