  This paper introduces link functions for transforming one probability
distribution to another such that the Kullback-Leibler and R\'enyi divergences
between the two distributions are symmetric. Two general classes of link models
are proposed. The first model links two survival functions and is applicable to
models such as the proportional odds and change point, which are used in
survival analysis and reliability modeling. A prototype application involving
the proportional odds model demonstrates advantages of symmetric divergence
measures over asymmetric measures for assessing the efficacy of features and
for model averaging purposes. The advantages include providing unique ranks for
models and unique information weights for model averaging with one-half as much
computation requirement of asymmetric divergences. The second model links two
cumulative probability distribution functions. This model produces a
generalized location model which are continuous counterparts of the binary
probability models such as probit and logit models. Examples include the
generalized probit and logit models which have appeared in the survival
analysis literature, and a generalized Laplace model and a generalized
Student-$t$ model, which are survival time models corresponding to the
respective binary probability models. Lastly, extensions to symmetric
divergence between survival functions and conditions for copula dependence
information are presented.
