Introduction

Innovation modeling is a framework for constructing stochastic representations of phenomena, where it is assumed that, at a fundamental level, the phenomenon under investigation is composed of independent atoms of randomness or "innovations" that go through a process of "mixing". In certain cases, the assumption of independence in such models may be derived from physical considerations but, most often, it is based on the level of complexity one is willing or able to incorporate in the model while meeting computational and/or analytical limitations. In any case, this modeling principle can be employed in order to interpret, guide, and inform the design of schemes and algorithms for treating data obtained from observation, with applications in many areas of science and engineering including biomedical imaging and reconstruction.