Controlling the flexibility of non‑Gaussian processes through shrinkage priors
Published in Bayesian Analysis, 2022
Recommended citation: Cabral, R., Bolin, D. and Rue, H. (2022). "Controlling the flexibility of non‑Gaussian processes through shrinkage priors." Bayesian Analysis.
Inferential procedures tend to overestimate the degree of non-Gaussianity in the data and therefore we construct priors that contract the model towards Gaussianity.
In our venture to derive sensible priors, we also propose a new intuitive parameterization of the non-Gaussian models and discuss how to implement them efficiently in Stan. The methods are derived for a generic class of non-Gaussian models that include spatial Matérn fields, autoregressive models for time series, and simultaneous autoregressive models for aerial data.
More resources:
- Short vignette about non-Gaussian processes and how to implement them on Stan
- Bookdown containing the code of several applications to time series, geostatistical and areal data
- Stan Connect 2022 presentation
- arXiv version
Recommended citation: Cabral, R., Bolin, D. and Rue, H. (2022). “Controlling the flexibility of non‑Gaussian processes through shrinkage priors.” Bayesian Analysis.