We present a probabilistic framework for correspondence and egomotion. First, we suggest computing probability distributions of correspondence. This has the advantage of being robust to points subject to the aperture effect and repetitive structure, while giving up no information at feature points. Additionally, correspondence probability distributions can be computed for every point in the scene. Next, we generate a probability distribution over the motions, from these correspondence probability distributions, through a probabilistic notion of the epipolar constraint. Finding the maximum in this distribution is shown to be a generalization of least-squared epipolar minimization. We will show that because our technique allows so much correspondence information to be extracted, more accurate egomotion estimation is possible.