  The prediction of transition temperatures can be regarded in several ways,
either as an exacting test of theory, or as a tool for identifying theoretical
rules for defining new homology models. Popular "first principle" methods for
predicting transition temperatures in conventional crystalline superconductors
have failed for cuprate HTSC, as have parameterized models based on CuO2 planes
(with or without apical oxygen). Following a path suggested by Bayesian
probability, we find that the glassy, self-organized dopant network percolative
model is so successful that it defines a new homology class appropriate to
ceramic superconductors. The reasons for this success are discussed, and a
critical comparison is made with previous theories. The predictions are
successful for all ceramics, including new non-cuprates based on FeAs in place
of CuO2.
