Where Bayes tweaks Gauss: Conditionally Gaussian priors for stable multi-dipole estimation
arXiv preprint arXiv:2006.04141, 2020•arxiv.org
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 …
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 …
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.
arxiv.org
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