  Maximum likelihood fits to data can be done using binned data (histograms)
and unbinned data. With binned data, one gets not only the fitted parameters
but also a measure of the goodness of fit. With unbinned data, currently, the
fitted parameters are obtained but no measure of goodness of fit is available.
This remains, to date, an unsolved problem in statistics. Using Bayes theorem
and likelihood ratios, we provide a method by which both the fitted quantities
and a measure of the goodness of fit are obtained for unbinned likelihood fits,
as well as errors in the fitted quantities. We provide an ansatz for
determining Bayesian a priori probabilities.
