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The Asymptotic Equipartition Property

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Introduction   ::   Convergence   ::   Data Compression   ::   Sets

Convergence in probability

Before jumping intro the theorem we need to discuss what it means to converge in probability . There are a variety of ways that things can converge in the world of probability. There is convergence in distribution (as exhibited by the central limit theorem), there is convergence almost surely (which is very strong), there is convergence in mean square , and there is convergence in probability . We could spend about three weeks working through what each of these mean, and which implies the other. However, for the moment we will simply focus on convergence in probability.


\begin{definition}A sequence $X_1,X_2,\ldots,X_n$\ is said to
{\bf converge in ...
...\rightarrow 0 \text{ as } n\rightarrow
\infty.
\end{displaymath}\end{definition}
Recalling the definition of convergence of sequences, we can say this as follows: For any $\epsilon >0$ and for any $\delta > 0$ , there is an n 0 such that

\begin{displaymath}P[\vert X - X_n\vert < \epsilon] > 1-\delta
\end{displaymath}

for all n > n 0 .


\begin{example}
Let $X_i$\ be a sequence of iid random variables and let
\begin...
...h}More briefly we might write $S_n \rightarrow S$\ in probability.
\end{example}
One way of quantifying how we are doing on the convergence is by means of Markov's inequality: For a positive r.v. and any $\delta > 0$ ,

\begin{displaymath}P[X \geq \epsilon] \leq \frac{EX}{\delta}.
\end{displaymath}

From this we can derive the Chebyshev inequality : for a r.v. Y with mean $\mu$ and variance $\sigma^2$

\begin{displaymath}P[\vert Y - \mu\vert > \epsilon] \leq \frac{\sigma^2}{\epsilon^2}.
\end{displaymath}

From this we can show convergence of the sample mean (the WLLN).

Now the AEP:
\begin{theorem}
(AEP) If $X_1,X_2,\ldots,X_n$\ are iid $\sim p(x)$\ then
\begin...
...,X_2,\ldots,X_n) \rightarrow H(X)
\end{displaymath}in probability.
\end{theorem}


\begin{proof}
Since the $X_i$\ are iid, so are the $\log p(X_i)$. By independen...
...(X)\text{ in probability} \\
&= H(X).
\end{aligned}\end{displaymath}\end{proof}

The set of sequences that come up most often (according the probability law of the r.v.) are typical sequences . The typicality is defined as follows:
\begin{definition}
The {\bf typical set} $A_\epsilon^{(n)}$\ with respect to $p(...
..._1,x_2,\ldots,x_n) \leq
2^{-n(H(X)-\epsilon)}.
\end{displaymath}\end{definition}
So the typical sequences occur with a probability that is in the neighborhood of 2 - nH .
\begin{example}
Returning to the first example, $H(X) = 0.88129$, and $nH(X) =
...
...o the probability with which the sequence with three ones
occurs.
\end{example}
The typical set $A_\epsilon^{(n)}$ has the following properties:
\begin{theorem}
\begin{enumerate}
\item If $(x_1,x_2,\ldots,x_n) \in A_\epsilo...
...2^{n(H(X)-\epsilon)}$ for $n$\ sufficiently large.
\end{enumerate}\end{theorem}
Interpretations: By (1), nearly all of the elements in the typical set are nearly equiprobable. By (2), the typical set occurs with probability near 1 (that is why it is typical!). By (3) and (4), the number of elements in the typical set is nearly 2 nH .


\begin{proof}
\begin{enumerate}
\item Take $-\frac{1}{n} \log_2$\ of the defin...
... A_\epsilon^{(n)}\vert.
\end{aligned}\end{displaymath}\end{enumerate}\end{proof}

Copyright 2008, by the Contributing Authors. Cite/attribute Resource . admin. (2006, May 17). The Asymptotic Equipartition Property. Retrieved January 07, 2011, from Free Online Course Materials — USU OpenCourseWare Web site: http://ocw.usu.edu/Electrical_and_Computer_Engineering/Information_Theory/equi_1.htm. This work is licensed under a Creative Commons License Creative Commons License