In Section 13.3, the prior for each one of the unknown parameters, θ_{k},k = 0,1,…,K − 1, were given the liberty to have their own variances, ${\sigma}_{k}^{2}:=\frac{1}{{\alpha}_{k}}$. 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 ...

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