  After a decade of extensive study of the sparse representation synthesis
model, we can safely say that this is a mature and stable field, with clear
theoretical foundations, and appealing applications. Alongside this approach,
there is an analysis counterpart model, which, despite its similarity to the
synthesis alternative, is markedly different. Surprisingly, the analysis model
did not get a similar attention, and its understanding today is shallow and
partial. In this paper we take a closer look at the analysis approach, better
define it as a generative model for signals, and contrast it with the synthesis
one. This work proposes effective pursuit methods that aim to solve inverse
problems regularized with the analysis-model prior, accompanied by a
preliminary theoretical study of their performance. We demonstrate the
effectiveness of the analysis model in several experiments.
