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What is Learnable in Valiant's Theory of the Learnable? 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Probability Density Functions from the Fisher Information Metric
T. Clingman, Jeff Murugan, Jonathan P. Shock · 2015-04-13 · via math.ST updates on arXiv.org

We show a general relation between the spatially disjoint product of probability density functions and the sum of their Fisher information metric tensors. We then utilise this result to give a method for constructing the probability density functions for an arbitrary Riemannian Fisher information metric tensor. We note further that this construction is extremely unconstrained, depending only on certain continuity properties of the probability density functions and a select symmetry of their domains.

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