We recast the problem in the framework of estimation theory, using probability distributions to describe successive intermediate representations of motion information. Previously, researchers have described a mapping from images to distribution of motion energy (analogous to the outputs of direction selective cortical cells), and then to distributed representations of of image velocity (analogous to the outputs of MT cells). We discuss an extension of the Heeger/Jepson algorithms that computes a distributed representation of image velocity, thus avoiding the biological implausibility of explicit velocity representations. Uncertainties and ambiguities due to noise or the aperture problem may be directly included in the distributions of motion energy, and propagated through the computation to alter the final distribution of the 3D motion parameters. We also discuss extensions to handle situations involving motion transparency, motion occlusion boundaries, and independently moving objects.