- Joint entropy
The

**joint entropy**is an entropy measure used ininformation theory . The joint entropy measures how much entropy is contained in a joint system of tworandom variables . If the random variables are $X$ and $Y$, the joint entropy is written $H(X,Y)$. Like other entropies, the joint entropy can be measured inbit s, nits, or hartleys depending on the base of thelogarithm .**Background**Given a random variable $X$, the entropy $H(X)$ describes our uncertainty about the value of $X$. If $X$ consists of several events $x$, which each occur with probability $p\_x$, then the entropy of $X$ is

:$H(X)\; =\; -sum\_x\; p\_x\; log\_2(p\_x)\; !$

Consider another random variable $Y$, containing events $y$ occurring with probabilities $p\_y$. $Y$ has entropy $H(Y)$.

However, if $X$ and $Y$ describe related events, the total entropy of the system may not be $H(X)+H(Y)$. For example, imagine we choose an

integer between 1 and 8, with equal probability for each integer. Let $X$ represent whether the integer is even, and $Y$ represent whether the integer is prime. One-half of the integers between 1 and 8 are even, and one-half are prime, so $H(X)=H(Y)=1$. However, if we know that the integer is even, there is only a 1 in 4 chance that it is also prime; the distributions are related. The total entropy of the system is less than 2 bits. We need a way of measuring the total entropy of both systems.**Definition**We solve this by considering each "pair" of possible outcomes $(x,y)$. If each pair of outcomes occurs with probability $p\_\{x,y\}$, the joint entropy is defined as

:$H(X,Y)\; =\; -sum\_\{x,y\}\; p\_\{x,y\}\; log\_2(p\_\{x,y\})\; !$

In the example above we are not considering 1 as a prime. Then the joint probability distribution becomes:

$P(even,prime)=P(odd,not\; prime)=1/8\; quad$

$P(even,not\; prime)=P(odd,prime)=3/8\; quad$

Thus, the joint entropy is

$-2frac\{1\}\{8\}log\_2(1/8)\; -2frac\{3\}\{8\}log\_2(3/8)\; approx\; 1.8$ bits.

**Properties****Greater than subsystem entropies**The joint entropy is always at least equal to the entropies of the original system; adding a new system can never reduce the available uncertainty.

:$H(X,Y)\; geq\; H(X)$

This inequality is an equality if and only if $Y$ is a (deterministic) function of $X$.

if $Y$ is a (deterministic) function of $X$, we also have

:$H(X)\; geq\; H(Y)$

**ubadditivity**Two systems, considered together, can never have more entropy than the sum of the entropy in each of them. This is an example of

subadditivity .:$H(X,Y)\; leq\; H(X)\; +\; H(Y)$

This inequality is an equality if and only if $X$ and $Y$ are

statistically independent .**Bounds**Like other entropies, $H(X,Y)\; geq\; 0$ always.

**Relations to Other Entropy Measures**The joint entropy is used in the definitions of the

conditional entropy ::$H(X|Y)\; =\; H(X,Y)\; -\; H(Y),$

and the

mutual information ::$I(X;Y)\; =\; H(X)\; +\; H(Y)\; -\; H(X,Y),$

In

quantum information theory , the joint entropy is generalized into thejoint quantum entropy .**References**#

*Wikimedia Foundation.
2010.*

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