13.6 Sparse Bayesian Learning (SBL)

In Section 13.3, the prior for each one of the unknown parameters, θk,k = 0,1,…,K − 1, were given the liberty to have their own variances, σk2:=1αksi132_e. In turn, these variances were treated as hidden random variables and a prior was assigned to each of them in terms of a number of hyperparameters.

In [75, 81], the model was slightly modified. The concept of using different variances for the priors was retained, but the variances were treated as deterministic parameters and not as random ones.2 In this context, the task becomes a generalization of the one treated in Section 12.6, and it is built upon the following ...

Get Machine Learning now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.