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. 1308.0049
  4. Cited By
A composite likelihood approach to computer model calibration using
  high-dimensional spatial data

A composite likelihood approach to computer model calibration using high-dimensional spatial data

31 July 2013
Won Chang
M. Haran
R. Olson
K. Keller
ArXivPDFHTML

Papers citing "A composite likelihood approach to computer model calibration using high-dimensional spatial data"

3 / 3 papers shown
Title
Posterior exploration for computationally intensive forward models
Posterior exploration for computationally intensive forward models
D. Higdon
C. Fox
J. Moulton
Shane Reese
J. Vrugt
TPM
53
34
0
01 May 2024
An MCMC Approach to Classical Estimation
An MCMC Approach to Classical Estimation
Victor Chernozhukov
H. Hong
415
819
0
18 Jan 2023
Bayesian Inference from Composite Likelihoods, with an Application to
  Spatial Extremes
Bayesian Inference from Composite Likelihoods, with an Application to Spatial Extremes
M. Ribatet
D. Cooley
A. Davison
192
183
0
27 Nov 2009
1