2007IEEE Transactions on Instrumentation and MeasurementRequires access

The Principle of Maximum Entropy Applied in the Evaluation of the Measurement Uncertainty

G. Iuculano, Lars Nielsen, A. Zanobini, G. Pellegrini

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Abstract

The maximum entropy approach is a flexible and powerful tool for assigning a probability distribution to a measurable quantity treated as a random variable subjected to known moment constraints. The aim of this paper is to describe how the principle of maximum entropy may be used to transform information about the value of a quantity into a probability density function (pdf) that reflects exactly that information and nothing else. This principle will be applied to common cases of metrological interest, where different kinds of information are available. The derivation of the pdf is given in each case, and two practical examples with numerical results are reported to demonstrate the efficiency of the maximum entropy method

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What this paper is about

The maximum entropy approach is a flexible and powerful tool for assigning a probability distribution to a measurable quantity treated as a random variable subjected to known moment constraints. The aim of this paper is to describe how the principle of maximum entropy may be used to transform information about the value of a quantity into a probability density function (pdf) that reflects exactly that information and nothing else. This principle will be applied to common cases of metrological interest, where different kinds of information are available. The derivation of the pdf is given in each case, and two practical examples with numerical results are reported to demonstrate the efficiency of the maximum entropy method

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Available abstract

The maximum entropy approach is a flexible and powerful tool for assigning a probability distribution to a measurable quantity treated as a random variable subjected to known moment constraints. The aim of this paper is to describe how the principle of maximum entropy may be used to transform information about the value of a quantity into a probability density function (pdf) that reflects exactly that information and nothing else. This principle will be applied to common cases of metrological interest, where different kinds of information are available. The derivation of the pdf is given in each case, and two practical examples with numerical results are reported to demonstrate the efficiency of the maximum entropy method

Key concepts: Principle of maximum entropy, Maximum entropy spectral estimation, Maximum entropy probability distribution, Probability density function, Entropy (arrow of time), Random variable, Measurement uncertainty, Mathematics

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