In this paper we introduce a novel model for Gaussian process (GP) regression in the fully Bayesian setting. Motivated by the ideas of sparsification, ...
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Borrowing the framework of Bayesian (generalized) additive modeling (Hastie and Tibshirani, 1990, 2000), we propose the Sparse Additive Gaussian Process (SAGP).
Aug 23, 2019 · Abstract:In this paper we introduce a novel model for Gaussian process (GP) regression in the fully Bayesian setting.
A novel model for Gaussian process (GP) regression in the fully Bayesian setting, built around a recursive partitioning scheme, which mitigates the issue of ...
Sparse Additive Gaussian Process Regression (SAGP). The proposed SAGP model combines the three key ingredients of sparsification, Bayesian additive modeling ...
In this paper, we pro- pose a new sparse Gaussian process model with two additive components: FIC for the long length-scales and CS covariance func- tion for ...
This paper presents a novel variable selection method in additive nonparametric regression model. This work is motivated by the need to select the number of ...
Sparse Gaussian Process Regression (SGPR)¶. Overview¶. In this notebook, we'll overview how to use SGPR in which the inducing point locations are learned.
Apr 17, 2019 · We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data.
Apr 29, 2023 · In this section, we provide a brief introduction to GP regression and review some existing methods in. Bayesian optimization based on GPs. 2.1 ...