Gaussian Processes and Model Emulation

Marcel van Oijen

Chapter Chapter 15 in Bayesian Compendium, 2024, pp 105-117 from Springer

Abstract: Abstract Sampling-based estimation of the posterior distribution is computationally demanding. We have already mentioned the continuing search for efficient MCMC algorithms. MCMC is especially slow when the model of interest is a process-based model (PBM) with a long run-time. In such cases, it may be good to replace the PBM with a faster surrogate model. The surrogate model will take the same inputs as the original model but calculate the output more quickly. However, its output cannot be exactly the same as that of the original model, so it just provides an approximation. If the surrogate model is a statistical model that produces not just the approximative prediction of what the original model would have produced, but a whole probability distribution, then it is called a statistical emulator, or just emulator for short.

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-66085-6_15

Ordering information: This item can be ordered from
http://www.springer.com/9783031660856

DOI: 10.1007/978-3-031-66085-6_15

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().