Practical Hilbert space approximate Bayesian Gaussian processes for
probabilistic programming

Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming

23 April 2020

Gabriel Riutort-Mayol

Paul-Christian Burkner

Michael R. Andersen

Arno Solin

Papers citing "Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming"

7 / 7 papers shown

Title
Dynamic Online Ensembles of Basis Expansions Daniel Waxman Petar M. Djurić 27 3 0 02 May 2024
Generalised Linear Mixed Model Specification, Analysis, Fitting, and Optimal Design in R with the glmmr Packages S. Watson 13 3 0 22 Mar 2023
GaussED: A Probabilistic Programming Language for Sequential Experimental Design Matthew A. Fisher Onur Teymur Chris J. Oates 24 1 0 15 Oct 2021
Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions P. Greengard M. O’Neil 20 10 0 12 Aug 2021
Pathfinder: Parallel quasi-Newton variational inference Lu Zhang Bob Carpenter A. Gelman Aki Vehtari 41 40 0 09 Aug 2021
On Information Gain and Regret Bounds in Gaussian Process Bandits Sattar Vakili Kia Khezeli Victor Picheny GP 21 125 0 15 Sep 2020
MCMC using Hamiltonian dynamics Radford M. Neal 176 3,260 0 09 Jun 2012