Fitting latent non-Gaussian models using variational Bayes and Laplace approximations
Published in Journal of the American Statistical Association, 2022
Recommended citation: Cabral, R., Bolin, D. and Rue, H. (2024). "Fitting latent non-Gaussian models using variational Bayes and Laplace approximations. " Journal of the American Statistical Association 1-13.
In this paper, we derive variational Bayes algorithms for fast and scalable inference of latent non-Gaussian models. To facilitate Bayesian inference, we introduce the ngvb package, where LGMs implemented in R-INLA can be easily extended to LnGMs by adding a single line of code.
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Recommended citation: Cabral, R., Bolin, D. and Rue, H. (2024). “Fitting latent non-Gaussian models using variational Bayes and Laplace approximations.” Journal of the American Statistical Association 1-13.