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Bayesian Latent Gaussian Models

In: Statistical Modeling Using Bayesian Latent Gaussian Models

Author

Listed:
  • Birgir Hrafnkelsson

    (University of Iceland)

  • Haakon Bakka

    (Norwegian Veterinary Institute)

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.

Suggested Citation

  • Birgir Hrafnkelsson & Haakon Bakka, 2023. "Bayesian Latent Gaussian Models," Springer Books, in: Birgir Hrafnkelsson (ed.), Statistical Modeling Using Bayesian Latent Gaussian Models, pages 1-80, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-39791-2_1
    DOI: 10.1007/978-3-031-39791-2_1
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