Where Bayes tweaks Gauss: Conditionally Gaussian priors for stable multi-dipole estimation

Alessandro Viani; Gianvittorio Luria; Harald Bornfleth; Alberto Sorrentino

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arXiv · 2006.04141

Where Bayes tweaks Gauss: Conditionally Gaussian priors for stable multi-dipole estimation

Alessandro Viani·Gianvittorio Luria·Harald Bornfleth·Alberto Sorrentino

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Abstract

We present a very simple yet powerful generalization of a previously described model and algorithm for estimation of multiple dipoles from magneto/electro-encephalographic data. Specifically, the generalization consists in the introduction of a log-uniform hyperprior on the standard deviation of a set of conditionally linear/Gaussian variables. We use numerical simulations and an experimental dataset to show that the approximation to the posterior distribution remains extremely stable under a wide range of values of the hyperparameter, virtually removing the dependence on the hyperparameter.

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Alessandro Viani, Gianvittorio Luria, Harald Bornfleth, Alberto Sorrentino. 2020-06-07. Where Bayes tweaks Gauss: Conditionally Gaussian priors for stable multi-dipole estimation. https://arxiv.org/abs/2006.04141

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