Augmenting a high-fidelity GP's inputs with predictions from all low-fidelity surrogates improves accuracy and cuts cost versus cokriging and autoregressive multifidelity GPs on scarce-data problems.
Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data
Augmenting a high-fidelity GP's inputs with predictions from all low-fidelity surrogates improves accuracy and cuts cost versus cokriging and autoregressive multifidelity GPs on scarce-data problems.