ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1901.04827
  4. Cited By
Approximating Gaussian Process Emulators with Linear Inequality
  Constraints and Noisy Observations via MC and MCMC
v1v2 (latest)

Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC

15 January 2019
A. F. López-Lopera
François Bachoc
N. Durrande
Jérémy Rohmer
Déborah Idier
O. Roustant
ArXiv (abs)PDFHTML

Papers citing "Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC"

2 / 2 papers shown
Title
High-dimensional additive Gaussian processes under monotonicity
  constraints
High-dimensional additive Gaussian processes under monotonicity constraints
A. F. López-Lopera
François Bachoc
O. Roustant
81
9
0
17 May 2022
Posterior contraction rates for constrained deep Gaussian processes in
  density estimation and classication
Posterior contraction rates for constrained deep Gaussian processes in density estimation and classication
François Bachoc
A. Lagnoux
87
4
0
14 Dec 2021
1