Lang and I worked on the Comet Holmes project more today. We encountered an interesting issue when we tried to marginalize out the time at which an image was taken: If you think an image was taken of a comet (and we do), and you don't know either the orbit of the comet or the time at which the image was taken (and we don't, by construction), then you are inclined to infer a slowly-moving comet! This comes from the fact that the only sensible likelihood (probability of the image given the comet parameters) involves a marginalization over times, and more time gets into each image the slower the comet goes. A slow comet is a distant comet, and that is a less observable (and less likely to be observed) comet, so we are doing something wrong, but it is not trivial to find a principled solution to this one. Bayesians out there? This is a general issue for all parametric curve fitting is it not?