**8. Hierarchical prior models**

Even if simple Gaussian and non-Gaussian priors used in previous sections are of great importance and use in many applications, still they have, in many cases, limitations. For example, when we know that the signals have impulsive shapes or discontinuous or are piecewise continuous. The same limitations when we know, for example, that the images are composed of homogeneous regions with specified contours, or even, that the object under the test is composed of a limited number of homogeneous materials. Hierarchical models push farther these limitations of simple prior models. In the following, we consider three families of such hierarchical models: Sparsity aware models, Scaled Mixture models and Gauss-Markov-Potts models [10, 15–17].
