  We construct and propose the "Bayesian Validation Metric" (BVM) as a general
model validation and testing tool. We find the BVM to be capable of
representing all of the standard validation metrics (square error, reliability,
probability of agreement, frequentist, area, probability density comparison,
statistical hypothesis testing, and Bayesian model testing) as special cases
and find that it can be used to improve, generalize, or further quantify their
uncertainties. Thus, the BVM allows us to assess the similarities and
differences between existing validation metrics in a new light.
  The BVM has the capacity to allow users to invent and select models according
to novel validation requirements. We formulate and test a few novel compound
validation metrics that improve upon other validation metrics in the
literature. Further, we construct the BVM Ratio for the purpose of quantifying
model selection under user defined definitions of agreement in the presence or
absence of uncertainty. This construction generalizes the Bayesian model
testing framework.
