# Difference between revisions of "Information, rate of generation of"

A quantity characterizing the amount of information (cf. Information, amount of) emanating from an information source in unit time. The rate of generation of information of an information source $u$ with discrete time generating the communication $\xi = ( . . . , \xi _ {-} 1 , \xi _ {0} , \xi _ {1} , . . . )$, formed by a sequence of random variables $\{ {\xi _ {k} } : {k = . . . , - 1, 0, 1 , . . . } \}$ taking values from some discrete set $X$, is defined by the equation

$$\tag{* } \overline{H}\; ( u) = \ \lim\limits _ {n - k \rightarrow \infty } \ { \frac{1}{n - k } } H ( \xi _ {k} ^ {n} ),$$

if this limit exists. Here $H ( \xi _ {k} ^ {n} )$ is the entropy of the random variable $\xi _ {k} ^ {n} = ( \xi _ {k} \dots \xi _ {n} )$. The variable $\overline{H}\; ( u)$, defined by (*), is also called the entropy (in a symbol) of the information source.

In certain cases one can successfully prove the existence of the limit in (*) and calculate it explicitly, e.g. in the case of stationary sources. Explicit formulas for $\overline{H}\; ( u)$ have been obtained for stationary Markov sources and Gaussian sources. The concept of a rate of generation of information is closely related to that of redundancy of an information source.

If $u$ is a stationary ergodic information source with finite number of states, then the following property of asymptotic uniform distribution (the McMillan theorem, [1]) holds. Let $P ( x ^ {L} ) = {\mathsf P} \{ \xi ^ {L} = x ^ {L} \}$, where $x ^ {L} = ( x _ {1} \dots x _ {L} )$ are the values of $\xi ^ {L} = ( \xi _ {1} \dots \xi _ {L} )$ in an information interval of length $L$. For any $\epsilon , \delta > 0$, there is an $L _ {0} ( \epsilon , \delta )$ such that for all $L \geq L _ {0} ( \epsilon , \delta )$,

$${\mathsf P} \left \{ \left | { \frac{- \mathop{\rm log} P ( x ^ {L} ) }{L} } - \overline{H}\; ( u) \right | > \delta \right \} < \epsilon .$$

#### References

 [1] J. Wolfowitz, "Coding theorems of information theory" , Springer (1961) [2] R. Gallagher, "Information theory and reliable communication" , Wiley (1968) [3] A.A. Feinstein, "Foundations of information theory" , McGraw-Hill (1958)