Maximum Entropy and Bayesian Methods for Image and Spatial Analysis
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (17 March 2024) | Viewed by 941
Special Issue Editors
Interests: Bayesian statistics; spatial statistics; image analysis and image processing
2. Statistics Department, School of Science, Lorestan University, Khorramabad 68151-44316, Iran
Interests: entropy; maximum entropy method; information theory; Bayesian statistics; copula; optimization problems; inverse problems; mathematical algorithms
Special Issue Information
Dear Colleagues,
The maximum entropy framework (Jaynes, 1957a) is a cornerstone of statistical inference, and it has a privileged position as the only consistent method for combining different data into a single image. It allows us to incorporate extra, prior knowledge about the object being imaged and leads to the selection of a probability density function that is consistent with our knowledge and introduces no unwarranted information. Any probability density function satisfying the constraints that have smaller entropy will contain more information and, hence, less uncertainty.
In a Bayesian view, probabilities are seen as degrees of belief that are modified by information, which is refined as more information becomes available. In the presence of limited information, Bayesian probabilities are often easily assigned where conventional probabilities cannot.
Due to these properties, both maximum entropy and Bayesian approaches have been used massively in image analysis and processing as well as in spatial statistics, i.e., analysis of data observed in geographical space. The combination of Bayesian approaches with the maximum entropy method provides a great inference method.
This Special Issue will accept unpublished original research papers and comprehensive reviews on maximum entropy and Bayesian methods with applications on image data as well as on more general spatial data.
Prof. Dr. Volker J Schmid
Prof. Dr. Zahra Amini Farsani
Guest Editors
Manuscript Submission Information
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Keywords
- entropy
- information theory
- maximum entropy method
- Bayesian statistics
- spatial statistics
- image analysis and image processing
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