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Abstract:
We introduce a set of transformations on the set of all probability
distributions over a finite state space, and show that these
transformations are the only ones that preserve certain
elementary probabilistic relationships. This result provides
a new perspective on a variety of probabilistic inference
problems in which invariance considerations play a role.
Two particular applications we consider in this paper
are the development of an equivariance-based approach to
the problem of measure selection, and a new justification for
Haldane's prior as the distribution that encodes prior
ignorance about the parameter of a multinomial distribution.
