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Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming

Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming

Gabriel Riutort-Mayol
Paul-Christian Burkner
Michael R. Andersen
Arno Solin
Aki Vehtari
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Papers citing "Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming"

7 / 7 papers shown
Title
Dynamic Online Ensembles of Basis Expansions
Dynamic Online Ensembles of Basis Expansions
Daniel Waxman
Petar M. Djurić
30
3
0
02 May 2024
Generalised Linear Mixed Model Specification, Analysis, Fitting, and Optimal Design in R with the glmmr Packages
Generalised Linear Mixed Model Specification, Analysis, Fitting, and Optimal Design in R with the glmmr Packages
S. Watson
15
3
0
22 Mar 2023
GaussED: A Probabilistic Programming Language for Sequential Experimental Design
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
Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions
P. Greengard
M. O’Neil
22
10
0
12 Aug 2021
Pathfinder: Parallel quasi-Newton variational inference
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
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
MCMC using Hamiltonian dynamics
Radford M. Neal
182
3,262
0
09 Jun 2012