Corpus ID: 88517556

Intuitive principle-based priors for attributing variance in additive model structures

@article{Fuglstad2019IntuitivePP,
  title={Intuitive principle-based priors for attributing variance in additive model structures},
  author={Geir-Arne Fuglstad and Ingeborg Gullikstad Hem and Alexander Knight and H. Rue and Andrea Riebler},
  journal={arXiv: Methodology},
  year={2019}
}
Variance parameters in additive models are often assigned independent priors that are selected haphazardly from simple parametric families. We present a new framework for constructing joint priors for the variance parameters that treats the model structure as a whole. The focus is latent Gaussian models where penalised complexity priors can be computed exactly and generalised to a principled-based joint prior. The prior distributes the total variance of the model components to the individual… Expand
3 Citations

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