Abstract
Probabilistic methods to make inference from a random sample of observations should satisfy certain requirements for consistency. A principle of invariance under conditioning of the random variable domain is stated. Such a principle is formalized using the paradigm of a two-step inference method based on data. Invariance requirements are expressed both in the data encoding and probability distribution selection steps.
Distributions may be assigned by minimization of cross-entropy using fractile constraints encoding data and any reference distribution. The paper shows that this version of entropy method satisfies the data encoding and probability selection invariance requirements.
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© 1990 Kluwer Academic Publishers
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Solana, V. (1990). Consistency Principle for Data-Based Probabilistic Inference. In: Fougère, P.F. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0683-9_4
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DOI: https://doi.org/10.1007/978-94-009-0683-9_4
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6792-8
Online ISBN: 978-94-009-0683-9
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