In exponential families of distributions, various divergence measures like Kullback-Leibler and Rényi divergences can be expressed using cumulant and mean functions. This also applies to entropy and affinity measures. By compiling existing representations, a unified approach to deriving these measures in exponential families is presented. These measures are useful for constructing confidence regions in scenarios involving multiple samples.