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lgpr: An interpretable nonparametric method for inferring covariate
  effects from longitudinal data
v1v2 (latest)

lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data

7 December 2019
Juho Timonen
Henrik Mannerstrom
Aki Vehtari
Harri Lähdesmäki
ArXiv (abs)PDFHTML

Papers citing "lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data"

4 / 4 papers shown
Title
Computationally efficient multi-level Gaussian process regression for
  functional data observed under completely or partially regular sampling
  designs
Computationally efficient multi-level Gaussian process regression for functional data observed under completely or partially regular sampling designs
Adam Gorm Hoffmann
C. T. Ekstrøm
Andreas Kryger Jensen
43
0
0
19 Jun 2024
Improving Neural Additive Models with Bayesian Principles
Improving Neural Additive Models with Bayesian Principles
Kouroche Bouchiat
Alexander Immer
Hugo Yèche
Gunnar Rätsch
Vincent Fortuin
BDLMedIm
105
6
0
26 May 2023
Additive Gaussian Processes Revisited
Additive Gaussian Processes Revisited
Xiaoyu Lu
A. Boukouvalas
J. Hensman
54
23
0
20 Jun 2022
Hierarchical Gaussian Processes with Wasserstein-2 Kernels
Hierarchical Gaussian Processes with Wasserstein-2 Kernels
S. Popescu
D. Sharp
James H. Cole
Ben Glocker
74
5
0
28 Oct 2020
1