Results 211 to 220 of about 122,856 (252)
Operations Research, 2006 This paper presents a method to assign utility values when only partial information is available about the decision maker’s preferences. We introduce the notion of a utility density function and a maximum entropy principle for utility assignment. The maximum entropy utility solution embeds a large family of utility functions that includes the most ...
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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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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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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 +2 more
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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 +2 more
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Optimization Letters, 2010
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Julia Piantadosi +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Julia Piantadosi +2 more
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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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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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Maximum Entropy Image Reconstruction
IEEE Transactions on Computers, 1977Two-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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Restoring with Maximum Likelihood and Maximum Entropy*
Journal of the Optical Society of America, 1972Given 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 OF ENTROPY FOR CREDAL SETS
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2003In 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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Common Sense and Maximum Entropy
Synthese, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Constrained Maximum-Entropy Sampling
Operations Research, 1998A 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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