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Foundations of Inference

We present a simple and clear foundation for finite inference that unites and significantly extends the approaches of Kolmogorov and Cox. Our approach is based on quantifying lattices of logical statements in a way that satisfies general lattice symmetries. With other applications such as measure theory in mind, our derivations assume minimal symmetries, relying on neither negation nor continuity nor differentiability. Each relevant symmetry corresponds to an axiom of quantification, and these axioms are used to derive a unique set of quantifying rules that form the familiar probability calculus. We also derive a unique quantification of divergence, entropy and information.

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Related contextRelated contextRelated contextRelated contextCo-authorshipRelated contextAuthorshipAuthorshipTopic signalTopic signalTopic signalTopic signalTopic signalTopic signalRelated contextWFoundations of Inferencepreprint / 2012AKevin H. KnuthResearcherAJohn SkillingResearcherTArtificial Intelligence22915 worksTmath.PR7239 worksTmath.ST3384 worksTStatistics Theory3281 worksTphysics.data-an1229 worksTmath.LO1661 works
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Foundations of Inference

preprint / 2012

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