  This paper derives an objective Bayesian "prior" based on considerations of
entropy/information. By this means, it produces a quantitative measure of
goodness of fit (the "H-statistic") that balances higher likelihood against the
number of fitting parameters employed. The method is intended for
phenomenological applications where the underlying theory is uncertain or
unknown.
  For example, it can help decide whether the large angle anomalies in the CMB
data should be taken seriously.
  I am therefore posting it now, even though it was published before the arxiv
existed.
