Bayesian Latent Gaussian Models

Birgir Hrafnkelsson () and Haakon Bakka ()
Additional contact information
Birgir Hrafnkelsson: University of Iceland
Haakon Bakka: Norwegian Veterinary Institute

A chapter in Statistical Modeling Using Bayesian Latent Gaussian Models, 2023, pp 1-80 from Springer

Abstract: Abstract Bayesian latent Gaussian models are Bayesian hierarchical models that assign Gaussian prior densities to the latent parameters. In this chapter, we present three subclasses within the class of Bayesian latent Gaussian models, namely, Bayesian Gaussian–Gaussian models, Bayesian latent Gaussian models with a univariate link function, and Bayesian latent Gaussian models with a multivariate link function. The structure of each subclass is described along with methods to infer the parameters of these models. The construction of prior densities for the latent parameters and the hyperparameters is described. Several examples are given to demonstrate how to apply models from these subclasses to real datasets.

Date: 2023
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:978-3-031-39791-2_1

Ordering information: This item can be ordered from
http://www.springer.com/9783031397912

DOI: 10.1007/978-3-031-39791-2_1

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 ().