Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants

Quan Vu, Matthew Moores, Andrew Zammit-Mangion

Research output: Contribution to journal › Article › peer-review

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@article{d5293cbef2434578854331c37a88e218,

title = "Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants",

author = "Quan Vu and Matthew Moores and Andrew Zammit-Mangion",

note = "c 2023 International Society for Bayesian Analysis",

year = "2023",

doi = "10.1214/23-BA1400",

language = "English",

pages = "1--25",

journal = "Bayesian Analysis",

issn = "1936-0975",

publisher = "Carnegie Mellon University",

}

TY - JOUR

T1 - Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants

AU - Vu, Quan

AU - Moores, Matthew

AU - Zammit-Mangion, Andrew

N1 - c 2023 International Society for Bayesian Analysis

PY - 2023

Y1 - 2023

U2 - 10.1214/23-BA1400

DO - 10.1214/23-BA1400

M3 - Article

SN - 1936-0975

SP - 1

EP - 25

JO - Bayesian Analysis

JF - Bayesian Analysis

ER -