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- W2093612730 abstract "The problem of finding the maximum entropy solution which satisfies some poorly defined constraints is central to data processing. Hitherto all attempts to solve it have been conceptually somewhat incomplete. The authors consider the traditional Boltzmann-Gibbs approach to entropy. When data are available the various outcomes can be assigned a priori probabilities (or weights) according to how much each of them agrees with the measured distribution when errors are taken into account. The reasoning yields a statistic S- chi 2, which is a precise expression for the entropy. The maximum S- chi 2 solution defines a distribution uniquely. Even in circumstances when there is no information apart from those provided by the data, the inferred distribution differs from the given by the data mean values and bears a closer resemblance to the underlying population than do the raw data. Where additional constraints are present, this maximum entropy procedure is more effective in incorporating them than are traditional maximum likelihood techniques. S- chi 2 maximisation may also be carried out iteratively up to a certain limit. It offers the most advantage in situations when one has a thorough understanding of the inherent noise behaviour." @default.
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- W2093612730 date "1987-06-21" @default.
- W2093612730 modified "2023-09-26" @default.
- W2093612730 title "Maximum entropy in data analysis with error-carrying constraints" @default.
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- W2093612730 doi "https://doi.org/10.1088/0305-4470/20/9/025" @default.
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