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

Autor:	Viani, Alessandro, Luria, Gianvittorio, Bornfleth, Harald, Sorrentino, Alberto
Rok vydání:	2020
Předmět:	Statistics - Applications Quantitative Biology - Quantitative Methods Statistics - Methodology G.3 G.1.10
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
Externí odkaz:	http://arxiv.org/abs/2006.04141 Zobrazit plný text záznamu View this record from Arxiv