[PDF] How to use KL-divergence to construct conjugate priors, with well-defined non-informative limits, for the multivariate Gaussian | Semantic Scholar

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Corpus ID: 237513911

How to use KL-divergence to construct conjugate priors, with well-defined non-informative limits, for the multivariate Gaussian

@inproceedings{Brummer2021HowTU,
title={How to use KL-divergence to construct conjugate priors, with well-defined non-informative limits, for the multivariate Gaussian},
author={Niko Brummer},
year={2021}
}

The Wishart distribution is the standard conjugate prior for the precision of the multivariate Gaussian likelihood, when the mean is known—while the normal-Wishart can be used when the mean is also unknown. It is however not so obvious how to assign values to the hyperparameters of these distributions. In particular, when forming non-informative limits of these distributions, the shape (or degrees of freedom) parameter of the Wishart must be handled with care. The intuitive solution of directly… Expand

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