  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.
