The set of all possible visual images is enormous, but not all of these are equally likely to be encountered by your eye or a camera. This nonuniform distribution over the image space is believed to be exploited by biological visual systems, and can be used as an advantage in most applications in image processing and machine vision. For example, loosely speaking, when one observes a visual image that has been corrupted by some sort of noise, the process of estimating the original source image may be viewed as one of looking for the highest probability image that is ``close to'' the noisy observation. Image compression amounts to using a larger proportion of the available bits to encode those regions of the image space that are more likely. And problems such as resolution enhancement or image synthesis involve selecting (sampling) a high-probability image, subject to some set of constraints. Specific examples of these applications can be found in many chapters throughout this Guide.