Approximate Inference for Fully Bayesian Gaussian Process Regression

• URL: http://arxiv.org/abs/1912.13440v2
• Date: Mon, 6 Apr 2020 14:22:14 GMT
• Title: Approximate Inference for Fully Bayesian Gaussian Process Regression
• Authors: Vidhi Lalchand and Carl Edward Rasmussen
• Abstract summary: Learning in Gaussian Process models occurs through the adaptation of hyper parameters of the mean and the covariance function. An alternative learning procedure is to infer the posterior over hyper parameters in a hierarchical specification of GPs we call textitFully Bayesian Gaussian Process Regression (GPR) We analyze the predictive performance for fully Bayesian GPR on a range of benchmark data sets.
• Score: 11.47317712333228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function. The classical approach entails maximizing the marginal likelihood yielding fixed point estimates (an approach called \textit{Type II maximum likelihood} or ML-II). An alternative learning procedure is to infer the posterior over hyperparameters in a hierarchical specification of GPs we call \textit{Fully Bayesian Gaussian Process Regression} (GPR). This work considers two approximation schemes for the intractable hyperparameter posterior: 1) Hamiltonian Monte Carlo (HMC) yielding a sampling-based approximation and 2) Variational Inference (VI) where the posterior over hyperparameters is approximated by a factorized Gaussian (mean-field) or a full-rank Gaussian accounting for correlations between hyperparameters. We analyze the predictive performance for fully Bayesian GPR on a range of benchmark data sets.

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