  Despite the flexibility and popularity of mixture models, their associated
parameter spaces are often difficult to represent due to fundamental
identification problems. This paper looks at a novel way of representing such a
space for general mixtures of exponential families, where the parameters are
identifiable, interpretable, and, due to a tractable geometric structure, the
space allows fast computational algorithms to be constructed.
