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Open Access Publications from the University of California

A Bayesian characterization of relative entropy

  • Author(s): Baez, JC
  • Fritz, T
  • et al.

We give a new characterization of relative entropy, also known as the Kullback-Leibler divergence. We use a number of interesting categories related to probability theory. In particular, we consider a category FinStat where an object is a Finite set equipped with a probability distribution, while a morphism is a measure-preserving function f: X → Y together with a stochastic right inverse s: Y → X. The function f can be thought of as a measurement process, while s provides a hypothesis about the state of the measured system given the result of a measurement. Given this data we can define the entropy of the probability distribution on X relative to the prior' given by pushing the probability distribution on Y forwards along s. We say that s is optimal' if these distributions agree. We show that any convex linear, lower semicontinuous functor from FinStat to the additive monoid [0;∞] which vanishes when s is optimal must be a scalar multiple of this relative entropy. Our proof is independent of all earlier characterizations, but inspired by the work of Petz. © John C. Baez and Tobias Fritz, 2014.

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