In probability theory and information theory, the **mutual information** (sometimes known by the archaic term **transinformation**) of two random variables is a quantity that measures the mutual dependence of the two random variables. The most common unit of measurement of mutual information is the bit, when logarithms to the base 2 are used.

Read more about Mutual Information: Definition of Mutual Information, Relation To Other Quantities, Variations of Mutual Information, Applications of Mutual Information

### Other articles related to "mutual information, information":

**Mutual Information**

... many applications, one wants to maximize

**mutual information**(thus increasing dependencies), which is often equivalent to minimizing conditional entropy ... Examples include In telecommunications, the channel capacity is equal to the

**mutual information**, maximized over all input distributions ... hidden Markov models have been proposed based on the maximum

**mutual information**(MMI) criterion ...

**Mutual Information**

... In probability theory, and in particular,

**information**theory, the conditional

**mutual information**is, in its most basic form, the expected value of the

**mutual information**of two random variables given the value of ...

**Mutual Information**

... Gluck (1992) that the category utility is equivalent to the

**mutual information**... Ordinality does not matter, because the

**mutual information**is not sensitive to ordinality.) In what follows, a term such as or simply refers to the probability with which adopts the particular ... From the definition of

**mutual information**for discrete variables, the

**mutual information**between the aggregate feature variable and the category variable is given by where is the prior probability ...

... measures include cross-correlation,

**mutual information**, sum of squared intensity differences, and ratio image uniformity ...

**Mutual information**and normalized

**mutual information**are the most popular image similarity measures for registration of multimodality images ...

**Mutual Information**- Definition

... This can be simplified as Alternatively, we may write Conditional

**mutual information**can also be rewritten to show its relationship to

**mutual information**Conditioning on a third random ... as a basic building block for proving other inequalities in

**information**theory, in particular, those known as Shannon-type inequalities ... Like

**mutual information**, conditional

**mutual information**can be expressed as a Kullback-Leibler divergence Or as an expected value of simpler Kullback-Leibler divergences ...

### Famous quotes containing the words information and/or mutual:

“The family circle has widened. The worldpool of *information* fathered by the electric media—movies, Telstar, flight—far surpasses any possible influence mom and dad can now bring to bear. Character no longer is shaped by only two earnest, fumbling experts. Now all the world’s a sage.”

—Marshall McLuhan (1911–1980)

“Louise Bryant: I’m sorry if you don’t believe in *mutual* independence and free love and respect.

Eugene O’Neill: Don’t give me a lot of parlor socialism that you learned in the village. If you were mine, I wouldn’t share you with anybody or anything. It would be just you and me. You’d be at the center of it all. You know it would feel a lot more like love than being left alone with your work.”

—Warren Beatty (b. 1937)