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Joint entropy

In information theory, joint entropy is a measure of the uncertainty associated with a set of variables.
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Related topics

Related topics

20 relations
Chow–Liu treeConditional entropyConditional mutual informationCross entropy

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2016
2016
Spatial Correlations in Physical Unclonable Functions
Physical Unclonable Functions (PUFs) are circuits which extract a device dependent secret from inherently available… 
2013
2013
The Economics of Curiosity
We develop the hypothesis that an individual can get some value of information, even if they do not use the information for his… 
2012
2012
Entropy-based efficiency enhancement techniques for evolutionary algorithms
2011
2011
3-D Discontinuity Orientations using Combined Optical Imaging and LiDAR Techniques
The importance of the collection and analysis of data on discontinuities cannot be overemphasized. Problems which include… 
2011
2011
Robust control using recursive design method for flexible joint robot manipulators
A flexible joint robot manipulator can be regarded as a cascade of two subsystems: link dynamics and the motor dynamics. Using… 
2002
2002
Joint entropy-constrained multiterminal quantization
We study the design of entropy-constrained multiterminal quantizers for coding two correlated continuous sources. Two design… 
Highly Cited
1995
Highly Cited
1995
On structure and stability in stochastic programs with random technology matrix and complete integer recourse
For two-stage stochastic programs with integrality constraints in the second stage, we study continuity properties of the… 
1988
1988
Allometric Estimation Of The Productivity Of Phytoplankton Assemblages
An allometric model is used to estimate the productivity of natural phytoplankton assemblages. The 14C fixation rates of > 5 ~m… 
1987
1987
Higher order probabilities
A number of writers have supposed that for the full specification of belief, higher order probabilities are required. Some have… 
1970
1970
On the applicability of modern algebra techniques to adaptive pattern recognition
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