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What would the expectation value of the mutual information be?
This approach is known as mutual information based distance measure.
Adding all possible terms together gives the value for mutual information.
Mutual information measures how much more is known about one random value when given another.
If mutual information is zero, you cannot determine anything about one value when given another.
Mutual information does not change based on which random value is revealed.
This has led to the use of similarity measures such as mutual information.
Mutual information is used in medical imaging for image registration.
Several variations on mutual information have been proposed to suit various needs.
It is not easy to know if mutual information is significant or large.
Several different clustering systems based on mutual information have been proposed.
Since temperature and month are connected, their mutual information would be a lot larger than zero.
If mutual information is one, then knowing one value will exactly tell you the other.
Usually an attribute with high mutual information should be preferred to other attributes.
Sometimes random events seem to have a pattern in the short term, but overall there is no mutual information.
The weight is the conditional mutual information due to the arc.
If mutual information is large, there is likely some connection between the two things being looked at.
There has been little mathematical work done on the weighted mutual information and its properties, however.
These hints or changes in likelihood are explained and measured with mutual information.
In certain presentations, it is also formally equivalent to the mutual information, as discussed below.
Another popular approach is to scale features by the mutual information of the training data with the training classes.
Unlike the mutual information, the interaction information can be either positive or negative.
Mutual information can be used to quantify the dependency.
It is the quantum mechanical analog of Shannon mutual information.
It uses combination of thermodynamics and mutual information content scores.