Information Theory/Discrete Memoryless Sources

1. Information Theory
2. Entropy of Discrete Sources
3. Discrete Memoryless Sources

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# OVERVIEW OF THE FIRST MAIN CHAPTER #

This first chapter describes the calculation and the meaning of entropy.  According to the Shannonian information definition,  entropy is a measure of the mean uncertainty about the outcome of a statistical event or the uncertainty in the measurement of a stochastic quantity.  Somewhat casually expressed,  the entropy of a random quantity quantifies its  »randomness«.

In detail are discussed:

1. The  »information content«  of a symbol and the  »entropy«  of a discrete memoryless source,
2. the  »binary entropy function«  and its application to non-binary sources,
3. the entropy calculation for  »sources with memory«  and suitable approximations,
4. the special features of  »Markov sources«  regarding the entropy calculation,
5. the procedure for sources with a large number of symbols, for example  »natural texts«,
6. the  »entropy estimates«  according to Shannon and Küpfmüller.

Model and requirements

We consider a discrete message source  $\rm Q$, which gives a sequence  $\langle q_ν \rangle$  of symbols.

• For the variable  $ν = 1$, ... , $N$, where  $N$  should be sufficiently large.

• Each individual source symbol  $q_ν$  comes from a symbol set  $\{q_μ \}$  where  $μ = 1$, ... , $M$.  $M$  denotes the symbol set size:

$$q_{\nu} \in \left \{ q_{\mu} \right \}, \hspace{0.25cm}{\rm with}\hspace{0.25cm} \nu = 1, \hspace{0.05cm} \text{ ...}\hspace{0.05cm} , N\hspace{0.25cm}{\rm and}\hspace{0.25cm}\mu = 1,\hspace{0.05cm} \text{ ...}\hspace{0.05cm} , M \hspace{0.05cm}.$$

The figure shows a quaternary message source  $(M = 4)$  with alphabet  $\rm \{A, \ B, \ C, \ D\}$  and an exemplary sequence of length  $N = 100$.

Quaternary source

The following requirements apply:

• The quaternary source is fully described by  $M = 4$  symbol probabilities  $p_μ$.  In general it applies:

$$\sum_{\mu = 1}^M \hspace{0.1cm}p_{\mu} = 1 \hspace{0.05cm}.$$

• The message source is memoryless, i.e., the individual sequence elements are  »statistically independent of each other«:

$${\rm Pr} \left (q_{\nu} = q_{\mu} \right ) = {\rm Pr} \left (q_{\nu} = q_{\mu} \hspace{0.03cm} | \hspace{0.03cm} q_{\nu -1}, q_{\nu -2}, \hspace{0.05cm} \text{ ...}\hspace{0.05cm}\right ) \hspace{0.05cm}.$$

• Since the alphabet consists of symbols  $($and not of random variables$)$,  the specification of  »expected values«  $($linear mean, second moment, standard deviation, etc.$)$  is not possible here,  but also not necessary from an information-theoretical point of view.

These properties will now be illustrated with an example.

Relative frequencies as a function of  $N$

$\text{Example 1:}$  For the symbol probabilities of a quaternary source applies:

$$p_{\rm A} = 0.4 \hspace{0.05cm},\hspace{0.2cm}p_{\rm B} = 0.3 \hspace{0.05cm},\hspace{0.2cm}p_{\rm C} = 0.2 \hspace{0.05cm},\hspace{0.2cm} p_{\rm D} = 0.1\hspace{0.05cm}.$$

For an infinitely long sequence  $(N \to \infty)$

• the  »relative frequencies«  $h_{\rm A}$,  $h_{\rm B}$,  $h_{\rm C}$,  $h_{\rm D}$   ⇒   a-posteriori parameters

• were identical to the  »probabilities«  $p_{\rm A}$,  $p_{\rm B}$,  $p_{\rm C}$,  $p_{\rm D}$   ⇒   a-priori parameters.

With smaller  $N$  deviations may occur, as the adjacent table  $($result of a simulation$)$  shows.

• In the graphic above an exemplary sequence is shown with  $N = 100$  symbols.

• Due to the set elements  $\rm A$,  $\rm B$,  $\rm C$  and  $\rm D$  no mean values can be given.

However,  if you replace the symbols with numerical values,  for example  $\rm A \Rightarrow 1$,   $\rm B \Rightarrow 2$,   $\rm C \Rightarrow 3$,   $\rm D \Rightarrow 4$, then you will get after
    »time averaging«   ⇒   crossing line     or     »ensemble averaging«   ⇒   expected value formation

• for the  »linear mean«   ⇒   »first order moment«:

$$m_1 = \overline { q_{\nu} } = {\rm E} \big [ q_{\mu} \big ] = 0.4 \cdot 1 + 0.3 \cdot 2 + 0.2 \cdot 3 + 0.1 \cdot 4 = 2 \hspace{0.05cm},$$

• for the  »second order moment«:

$$m_2 = \overline { q_{\nu}^{\hspace{0.05cm}2} } = {\rm E} \big [ q_{\mu}^{\hspace{0.05cm}2} \big ] = 0.4 \cdot 1^2 + 0.3 \cdot 2^2 + 0.2 \cdot 3^2 + 0.1 \cdot 4^2 = 5 \hspace{0.05cm},$$

• for the  »standard deviation«  according to the  »Theorem of Steiner«:

$$\sigma = \sqrt {m_2 - m_1^2} = \sqrt {5 - 2^2} = 1 \hspace{0.05cm}.$$

Maximum entropy of a discrete source

$\text{Claude Elwood Shannon}$  defined in 1948 in the standard work of information theory  [Sha48]  the concept of information as  "decrease of uncertainty about the occurrence of a statistical event".

Let us make a mental experiment with  $M$  possible results, which are all equally probable:   $p_1 = p_2 = \hspace{0.05cm} \text{ ...}\hspace{0.05cm} = p_M = 1/M \hspace{0.05cm}.$

Under this assumption applies:

• Is  $M = 1$, then each individual attempt will yield the same result and therefore there is no uncertainty about the output.

• On the other hand, an observer learns about an experiment with  $M = 2$, for example the  »coin toss«  with the set of events  $\big \{\rm \boldsymbol{\rm Z}(ahl), \rm \boldsymbol{\rm W}(app) \big \}$  and the probabilities  $p_{\rm Z} = p_{\rm W} = 0. 5$, a gain in information.  The uncertainty regarding  $\rm Z$  resp.  $\rm W$  is resolved.

• In the experiment  »dice«  $(M = 6)$  and even more in  »roulette«  $(M = 37)$  the gained information is even more significant for the observer than in the  »coin toss«  when he learns which number was thrown or which ball fell.

• Finally it should be considered that the experiment  »triple coin toss«  with  $M = 8$  possible results  $\rm ZZZ$,  $\rm ZZW$,  $\rm ZWZ$,  $\rm ZWW$,  $\rm WZZ$,  $\rm WZW$,  $\rm WWZ$,  $\rm WWW$  provides three times the information as the single coin toss  $(M = 2)$.

The following definition fulfills all the requirements listed here for a quantitative information measure for equally probable events, indicated only by the symbol set size  $M$.

$\text{Definition:}$  The  »maximum average information content«   of a message source depends only on the symbol set size  $M$  and results in

$$H_0 = {\rm log}\hspace{0.1cm}M = {\rm log}_2\hspace{0.1cm}M \hspace{0.15cm} {\rm (in \ “bit")} = {\rm ln}\hspace{0.1cm}M \hspace{0.15cm}\text {(in “nat")} = {\rm lg}\hspace{0.1cm}M \hspace{0.15cm}\text {(in “Hartley")}\hspace{0.05cm}.$$

• Since  $H_0$  indicates the maximum value of the  $\text{entropy}$  $H$,  $H_\text{max}=H_0$  is also used in our tutorial as short notation.

Please note our nomenclature:

• The logarithm will be called  »log«  in the following, independent of the base.

• The relations mentioned above are fulfilled due to the following properties:

$${\rm log}\hspace{0.1cm}1 = 0 \hspace{0.05cm},\hspace{0.2cm} {\rm log}\hspace{0.1cm}37 > {\rm log}\hspace{0.1cm}6 > {\rm log}\hspace{0.1cm}2\hspace{0.05cm},\hspace{0.2cm} {\rm log}\hspace{0.1cm}M^k = k \cdot {\rm log}\hspace{0.1cm}M \hspace{0.05cm}.$$

• Usually we use the logarithm to the base  $2$   ⇒   »logarithm dualis«    $\rm (ld)$,  where the pseudo unit  "bit"  $($more precisely:  "bit/symbol"$)$  is then added:

$${\rm ld}\hspace{0.1cm}M = {\rm log_2}\hspace{0.1cm}M = \frac{{\rm lg}\hspace{0.1cm}M}{{\rm lg}\hspace{0.1cm}2} = \frac{{\rm ln}\hspace{0.1cm}M}{{\rm ln}\hspace{0.1cm}2} \hspace{0.05cm}.$$

• In addition, you can find in the literature some additional definitions, which are based on the natural logarithm  $\rm (ln)$  or the logarithm of the tens  $\rm (lg)$.

Information content and entropy

We now waive the previous requirement that all  $M$  possible results of an experiment are equally probable.  In order to keep the spelling as compact as possible, we define for this section only:

$$p_1 > p_2 > \hspace{0.05cm} \text{ ...}\hspace{0.05cm} > p_\mu > \hspace{0.05cm} \text{ ...}\hspace{0.05cm} > p_{M-1} > p_M\hspace{0.05cm},\hspace{0.4cm}\sum_{\mu = 1}^M p_{\mu} = 1 \hspace{0.05cm}.$$

We now consider the »information content«  of the individual symbols, where we denote the  "logarithm dualis"  with  $\log_2$:

$$I_\mu = {\rm log_2}\hspace{0.1cm}\frac{1}{p_\mu}= -\hspace{0.05cm}{\rm log_2}\hspace{0.1cm}{p_\mu} \hspace{0.5cm}{\rm (unit\hspace{-0.15cm}: \hspace{0.15cm}bit\hspace{0.15cm}or\hspace{0.15cm}bit/Symbol)} \hspace{0.05cm}.$$

You can see:

• Because of  $p_μ ≤ 1$  the information content is never negative.  In the borderline case  $p_μ \to 1$  goes  $I_μ \to 0$.

• However, for  $I_μ = 0$   ⇒   $p_μ = 1$   ⇒   $M = 1$  the information content is also  $H_0 = 0$.

• For decreasing probabilities  $p_μ$  the information content increases continuously:

$$I_1 < I_2 < \hspace{0.05cm} \text{ ...}\hspace{0.05cm} < I_\mu <\hspace{0.05cm} \text{ ...}\hspace{0.05cm} < I_{M-1} < I_M \hspace{0.05cm}.$$

$\text{Conclusion:}$  The more improbable an event is, the greater is its information content.  This fact is also found in daily life:

1. "6 right ones" in the lottery are more likely to be noticed than "3 right ones" or no win at all.
2. A tsunami in Asia also dominates the news in Germany for weeks as opposed to the almost standard Deutsche Bahn delays.
3. A series of defeats of Bayern Munich leads to huge headlines in contrast to a winning series.  With 1860 Munich exactly the opposite is the case.

However, the information content of a single symbol (or event) is not very interesting.  On the other hand one of the central quantities of information theory is obtained,

• by ensemble averaging over all possible symbols  $q_μ$  bzw. 

• by time averaging over all elements of the sequence  $\langle q_ν \rangle$.

$\text{Definition:}$  The  »entropy«  $H$  of a discrete source indicates the  »mean information content of all symbols«:

$$H = \overline{I_\nu} = {\rm E}\hspace{0.01cm}[I_\mu] = \sum_{\mu = 1}^M p_{\mu} \cdot {\rm log_2}\hspace{0.1cm}\frac{1}{p_\mu}= -\sum_{\mu = 1}^M p_{\mu} \cdot{\rm log_2}\hspace{0.1cm}{p_\mu} \hspace{0.5cm}\text{(unit: bit, more precisely: bit/symbol)} \hspace{0.05cm}.$$

The overline marks again a time averaging and  $\rm E[\text{...}]$  an ensemble averaging.

Entropy is among other things a measure for

• the mean uncertainty about the outcome of a statistical event,

• the  "randomness"  of this event,  and

• the average information content of a random variable.

Binary entropy function

At first we will restrict ourselves to the special case  $M = 2$  and consider a binary source, which returns the two symbols  $\rm A$  and  $\rm B$.  The symbol probabilities are   $p_{\rm A} = p$  and   $p_{\rm B} = 1 - p$.

For the entropy of this binary source applies:

$$H_{\rm bin} (p) = p \cdot {\rm log_2}\hspace{0.1cm}\frac{1}{\hspace{0.1cm}p\hspace{0.1cm}} + (1-p) \cdot {\rm log_2}\hspace{0.1cm}\frac{1}{1-p} \hspace{0.5cm}{\rm (unit\hspace{-0.15cm}: \hspace{0.15cm}bit\hspace{0.15cm}or\hspace{0.15cm}bit/symbol)} \hspace{0.05cm}.$$

This function is called  $H_\text{bin}(p)$  the  »binary entropy function«.  The entropy of a source with a larger symbol set size  $M$  can often be expressed using  $H_\text{bin}(p)$ .

$\text{Example 2:}$  The figure shows the binary entropy function for the values  $0 ≤ p ≤ 1$  of the symbol probability of  $\rm A$  $($or also of  $\rm B)$.  You can see:

Binary entropy function as a function of  $p$

• The maximum value  $H_\text{max} = 1\; \rm bit$  results for  $p = 0.5$, thus for equally probable binary symbols.  Then   $\rm A$  and  $\rm B$  contribute the same amount to the entropy.

• $H_\text{bin}(p)$  is symmetrical around  $p = 0.5$.  A source with  $p_{\rm A} = 0.1$  and  $p_{\rm B} = 0. 9$  has the same entropy  $H = 0.469 \; \rm bit$  as a source with  $p_{\rm A} = 0.9$  and  $p_{\rm B} = 0.1$.

• The difference  $ΔH = H_\text{max} - H$ gives  the  »redundancy«  of the source and  $r = ΔH/H_\text{max}$  the  »relative redundancy«.   In the example,  $ΔH = 0.531\; \rm bit$  and  $r = 53.1 \rm \%$.

• For  $p = 0$  this results in  $H = 0$, since the symbol sequence  $\rm B \ B \ B \text{...}$  can be predicted with certainty   ⇒   symbol set size only  $M = 1$.  The same applies to  $p = 1$   ⇒   symbol sequence  $\rm A \ A \ A \text{...}$.

• $H_\text{bin}(p)$  is always a  »concave function«,  since the second derivative after the parameter  $p$  is negative for all values of  $p$ :

$$\frac{ {\rm d}^2H_{\rm bin} (p)}{ {\rm d}\,p^2} = \frac{- 1}{ {\rm ln}(2) \cdot p \cdot (1-p)}< 0 \hspace{0.05cm}.$$

Non-binary sources

In the  "first section"  of this chapter we considered a quaternary message source  $(M = 4)$  with the symbol probabilities  $p_{\rm A} = 0. 4$,   $p_{\rm B} = 0.3$,   $p_{\rm C} = 0.2$  and  $p_{\rm D} = 0.1$.  This source has the following entropy:

$$H_{\rm quat} = 0.4 \cdot {\rm log}_2\hspace{0.1cm}\frac{1}{0.4} + 0.3 \cdot {\rm log}_2\hspace{0.1cm}\frac{1}{0. 3} + 0.2 \cdot {\rm log}_2\hspace{0.1cm}\frac{1}{0.2}+ 0.1 \cdot {\rm log}_2\hspace{0.1cm}\frac{1}{0.1}.$$

For numerical calculation, the detour via the decimal logarithm  $\lg \ x = {\rm log}_{10} \ x$  is often necessary, since the  "logarithm dualis"  ${\rm log}_2 \ x$  is mostly not found on pocket calculators.

$$H_{\rm quat}=\frac{1}{{\rm lg}\hspace{0.1cm}2} \cdot \left [ 0.4 \cdot {\rm lg}\hspace{0.1cm}\frac{1}{0.4} + 0.3 \cdot {\rm lg}\hspace{0.1cm}\frac{1}{0. 3} + 0.2 \cdot {\rm lg}\hspace{0.1cm}\frac{1}{0.2} + 0.1 \cdot {\rm lg}\hspace{0.1cm}\frac{1}{0.1} \right ] = 1.845\,{\rm bit} \hspace{0.05cm}.$$

$\text{Example 3:}$  Now there are certain symmetries between the symbol probabilities:

Entropy of binary source and quaternary source

$$p_{\rm A} = p_{\rm D} = p \hspace{0.05cm},\hspace{0.4cm}p_{\rm B} = p_{\rm C} = 0.5 - p \hspace{0.05cm},\hspace{0.3cm}{\rm with} \hspace{0.15cm}0 \le p \le 0.5 \hspace{0.05cm}.$$

In this case, the binary entropy function can be used to calculate the entropy:

$$H_{\rm quat} = 2 \cdot p \cdot {\rm log}_2\hspace{0.1cm}\frac{1}{\hspace{0.1cm}p\hspace{0.1cm} } + 2 \cdot (0.5-p) \cdot {\rm log}_2\hspace{0.1cm}\frac{1}{0.5-p}$$

$$\Rightarrow \hspace{0.3cm} H_{\rm quat} = 1 + H_{\rm bin}(2p) \hspace{0.05cm}.$$

The graphic shows as a function of  $p$

• the entropy of the quaternary source (blue)

• in comparison to the entropy course of the binary source (red).

For the quaternary source only the abscissa  $0 ≤ p ≤ 0.5$  is allowed.

⇒   You can see from the blue curve for the quaternary source:

• The maximum entropy  $H_\text{max} = 2 \; \rm bit/symbol$  results for  $p = 0.25$   ⇒   equally probable symbols:   $p_{\rm A} = p_{\rm B} = p_{\rm C} = p_{\rm A} = 0.25$.

• With  $p = 0$  the quaternary source degenerates to a binary source with  $p_{\rm B} = p_{\rm C} = 0. 5$,   $p_{\rm A} = p_{\rm D} = 0$   ⇒   $H = 1 \; \rm bit/symbol$.  Similar applies to $p = 0.5$.

• The source with  $p_{\rm A} = p_{\rm D} = 0.1$  and  $p_{\rm B} = p_{\rm C} = 0.4$  has the following characteristics (each with the pseudo unit "bit/symbol"):

    (1)   entropy:   $H = 1 + H_{\rm bin} (2p) =1 + H_{\rm bin} (0.2) = 1.722,$

    (2)   Redundancy:   ${\rm \Delta }H = {\rm log_2}\hspace{0.1cm} M - H =2- 1.722= 0.278,$

    (3)   relative redundancy:   $r ={\rm \Delta }H/({\rm log_2}\hspace{0.1cm} M) = 0.139\hspace{0.05cm}.$

• The redundancy of the quaternary source with  $p = 0.1$  is  $ΔH = 0.278 \; \rm bit/symbol$   ⇒   exactly the same as the redundancy of the binary source with  $p = 0.2$.

Exercises for the chapter

Exercise 1.1: Entropy of the Weather

Exercise 1.1Z: Binary Entropy Function

Exercise 1.2: Entropy of Ternary Sources