Results 231 to 240 of about 563,674 (272)

MAXIMUM ENTROPY REVISITED

Statistica Neerlandica, 1984
AbstractMaximum entropy or minumum information distributions have their flexibilities severely curtailed if R or R+ is the domain and α is imposed as a Lebesgue measure. Tremendous flexibility is gained by removing these restrictions.
Mukherjee, D., Hurst, D. C.
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Maximum entropy deconvolution

ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
Burg's maximum entropy method has been used with success in spectral estimation. This paper is an attempt to generalize the maximum entropy method to the deconvolution of positive signals from a finite set of linear measurements. In this paper, we also investigate the existence of a solution to the deconvolution problem using a geometric approach.
Cheung Auyeung, Russell M. Mersereau, Ronald W. Schafer   +2 more
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Copulas with maximum entropy

Optimization Letters, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Julia Piantadosi, Phil G. Howlett, Jonathan M. Borwein   +2 more
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Maximum entropy tomography

Applied Optics, 1986
In this paper we discuss the use of the maximum entropy principle in tomographic reconstruction. We emphasize that entropy maximization is the only consistent regularization technique for images. A method of encoding prior information is discussed, and an example of the use of prior knowledge in the restoration of a fuel pin bundle is given.
S F, Gull, T J, Newton
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Restoring with Maximum Likelihood and Maximum Entropy*

Journal of the Optical Society of America, 1972
Given M sampled image values of an incoherent object, what can be deduced as the most likely object? Using a communication-theory model for the process of image formation, we find that the most likely object has a maximum entropy and is represented by a restoring formula that is positive and not band limited.
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Maximum Entropy Image Reconstruction

IEEE Transactions on Computers, 1977
Two-dimensional digital image reconstruction is an important imaging process in many of the physical sciences. If the data are insufficient to specify a unique reconstruction, an additional criterion must be introduced, either implicitly or explicitly before the best estimate can be computed. Here we use a principle of maximum entropy, which has proven
Stephen J. Wernecke, Larry R. D'Addario
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Common Sense and Maximum Entropy

Synthese, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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MAXIMUM OF ENTROPY FOR CREDAL SETS

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2003
In belief functions, there is a total measure of uncertainty that quantify the lack of knowledge and verifies a set of important properties. It is based on two measures: maximum of entropy and non-specificity. In this paper, we prove that the maximum of entropy verifies the same set of properties in a more general theory as credal sets and we present ...
Joaquín Abellán, Serafín Moral
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Constrained Maximum-Entropy Sampling

Operations Research, 1998
A fundamental experimental design problem is to select a most informative subset, having prespecified size, from a set of correlated random variables. Instances of this problem arise in many applied domains such as meteorology, environmental statistics, and statistical geology.
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Entropy estimation using the principle of maximum entropy

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
In this paper, we present a novel entropy estimator for a given set of samples drawn from an unknown probability density function (PDF). Counter to other entropy estimators, the estimator presented here is parametric. The proposed estimator uses the maximum entropy principle to offer anm-term approximation to the underlying distribution and does not ...
Behrouz Behmardi, Raviv Raich, Alfred O. Hero III   +2 more
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