Where Bayes tweaks Gauss: Conditionally Gaussian priors for stable multi-dipole estimation
Autor: | Viani, Alessandro, Luria, Gianvittorio, Bornfleth, Harald, Sorrentino, Alberto |
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Rok vydání: | 2020 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | 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. Comment: 23 pages, 8 figures |
Databáze: | arXiv |
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