Approximate Inference for Fully Bayesian Gaussian Process Regression

31 December 2019

Papers citing "Approximate Inference for Fully Bayesian Gaussian Process Regression"

13 / 13 papers shown

Title
Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo Hester Huijsdens D. Leeftink Linda Geerligs Max Hinne 32 0 0 07 Jun 2024
Data-driven Prior Learning for Bayesian Optimisation Sigrid Passano Hellan Christopher G. Lucas Nigel H. Goddard 26 0 0 24 Nov 2023
Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes Gledson Rodrigo Tondo Sebastian Rau I. Kavrakov Guido Morgenthal 27 6 0 21 Sep 2023
Scalable Bayesian Transformed Gaussian Processes Xinran Zhu Leo Huang Cameron Ibrahim E. Lee D. Bindel 8 1 0 20 Oct 2022
Bayesian Active Learning with Fully Bayesian Gaussian Processes Christoffer Riis Francisco Antunes F. B. Hüttel C. L. Azevedo Francisco Câmara Pereira GP 11 22 0 20 May 2022
A Bayesian Approach To Graph Partitioning Farshad Noravesh 16 0 0 24 Apr 2022
A novel sampler for Gauss-Hermite determinantal point processes with application to Monte Carlo integration Nicholas P. Baskerville 8 0 0 15 Mar 2022
Bayesian Optimisation for Active Monitoring of Air Pollution Sigrid Passano Hellan Christopher G. Lucas Nigel H. Goddard 11 10 0 15 Feb 2022
Validating Gaussian Process Models with Simulation-Based Calibration John Mcleod F. Simpson 14 3 0 27 Oct 2021
Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions P. Greengard M. O’Neil 25 10 0 12 Aug 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 21 107 0 24 Feb 2021
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation Ryan-Rhys Griffiths Alexander A. Aldrick Miguel García-Ortegón Vidhi R. Lalchand A. Lee 31 35 0 17 Oct 2019
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,266 0 09 Jun 2012