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Fitting a Gaussian Model to Aperture Synthesis Data by Akaike’s Information Criterion (AIC)
Published online by Cambridge University Press: 12 April 2016
Abstract
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The problem of determining the optimum number of components in fitting a Gaussian model to aperture synthesis data is considered. As a measure of the badness of the fitted model, we propose the use of Akaike’s Information Criterion (AIC).
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- Part VI: Other Image Improvement Methods
- Information
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- Copyright © Reidel 1979
References
Akaike, H.: 1973, Information Theory and an Extension of the Maximum Likelihood Principle, in 2nd International Symposium on Information Theory (Petrov, B.N. and Csaki, F., eds) pp.207–281, Akademiai Kiado, Budapest.Google Scholar
Akaike, H.: 1977, On Entropy Maximization Principle, in APPLICATION OF STATISTICS (Krishnaiah, P.R., ed.) pp.27–41, North-Holland Publishing Company.Google Scholar
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