Towards a unified characterization of MIMO mutual information distribution : a new Painlevé based approach

2012 ◽  
Author(s):  
Shang Li
Keyword(s):  
Mutual Information ◽  
Information Distribution

scholarly journals Interpretation of multi-scale permeability data through an information theory perspective

2020 ◽  
Vol 24 (6) ◽  
pp. 3097-3109
Author(s):  
Aronne Dell'Oca ◽  
Alberto Guadagnini ◽  
Monica Riva
Keyword(s):  
Information Theory ◽  
Mutual Information ◽  
Information Content ◽  
Gas Permeability ◽  
Measurement Scale ◽  
Measurement Scales ◽  
Multi Scale ◽  
Multivariate Mutual Information ◽  
Permeability Data

Abstract. We employ elements of information theory to quantify (i) the information content related to data collected at given measurement scales within the same porous medium domain and (ii) the relationships among information contents of datasets associated with differing scales. We focus on gas permeability data collected over Berea Sandstone and Topopah Spring Tuff blocks, considering four measurement scales. We quantify the way information is shared across these scales through (i) the Shannon entropy of the data associated with each support scale, (ii) mutual information shared between data taken at increasing support scales, and (iii) multivariate mutual information shared within triplets of datasets, each associated with a given scale. We also assess the level of uniqueness, redundancy and synergy (rendering, i.e., information partitioning) of information content that the data associated with the intermediate and largest scales provide with respect to the information embedded in the data collected at the smallest support scale in a triplet. Highlights. Information theory allows characterization of the information content of permeability data related to differing measurement scales. An increase in the measurement scale is associated with quantifiable loss of information about permeability. Redundant, unique and synergetic contributions of information are evaluated for triplets of permeability datasets, each taken at a given scale.

scholarly journals Mutual Information Loss in Pyramidal Image Processing

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 322 ◽  
Author(s):  
Jerry Gibson ◽  
Hoontaek Oh
Keyword(s):  
Image Analysis ◽  
Mutual Information ◽  
Probability Distributions ◽  
Reconstruction Error ◽  
Information Loss ◽  
Gaussian Pyramid ◽  
The Difference ◽  
Two Stages ◽  
Log Ratio

Gaussian and Laplacian pyramids have long been important for image analysis and compression. More recently, multiresolution pyramids have become an important component of machine learning and deep learning for image analysis and image recognition. Constructing Gaussian and Laplacian pyramids consists of a series of filtering, decimation, and differencing operations, and the quality indicator is usually mean squared reconstruction error in comparison to the original image. We present a new characterization of the information loss in a Gaussian pyramid in terms of the change in mutual information. More specifically, we show that one half the log ratio of entropy powers between two stages in a Gaussian pyramid is equal to the difference in mutual information between these two stages. We show that this relationship holds for a wide variety of probability distributions and present several examples of analyzing Gaussian and Laplacian pyramids for different images.

scholarly journals The characterization of hippocampal theta-driving neurons — a time-delayed mutual information approach

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Songting Li ◽  
Jiamin Xu ◽  
Guifen Chen ◽  
Longnian Lin ◽  
Douglas Zhou ◽  
...  
Keyword(s):  
Mutual Information ◽  
Information Approach ◽  
Hippocampal Theta
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