Probabilistic neural transfer function estimation with Bayesian system identification
Fig 2
Hyperparameter βv for regulating weight sparseness.
(a) Distribution of the means (μ) of model weights for different βv values. Dotted lines indicate distribution means. (b) Ratio of mass volume near zero for the distributions in (a). Note that with our setup, if we use a mixture of two Gaussians for the posterior, we would not observe a higher weight sparseness with a larger βv; rather, we would observe a wider distribution of model parameters.