# there is no mutual information without entropy

The concept of *entropy* is itself confusing and, so far, with high entropy.
When I associate it with the concept of mutual information, its entropy
decreases. Alright, I got my chance to confuse the reader and that was
actually fun.

*Entropy* is one of the most confusing concepts that has been
borrowed by computer scientists from physicists studying thermodynamics.
Just ignore for the next two lines what *entropy* should be. Think about it as
something that measures something else.

Now, imagine a bunch of molecules in a glass at time , temperature and pressure . The entropy of that system would be .
As the temperature decreases, the molecules slow down
and tend to stabilise to a fixed position. As time goes, the entropy of the
system decreases and the information, as the *“certainty”* of the exact
position of each molecule increases. This can be extended to an extreme case
in which the temperature is so low (absolute zero) that all the molecules
remain in a position that we can measure exactly. In that case we are not only
100% sure that the measured position is the real one, but also that the system
cannot come into a different configuration. The entropy of such a system is at
its minimum. No uncertainty. No alternative configurations.

As the temperature decreases, the molecules slow down and tend to stabilise to a fixed position. That’s when entropy is at its minimum

Since we’re not doing physics here, let’s go back to planet earth and do some information theory.
The concept of *entropy* is somehow linked to the **amount of uncertainty** of a system and to the amount of *information* that is present in a random signal.
The entropy at a source that emits a signal with probability is given by

If the message can be represented by an alphabet of symbols,
and the source emits symbols with , the entropy at
the source is
.
Usually, the term is referred to as and called
**information**.

A quite simple explanation of this is that a very frequent symbol (for which would be high) contains little information; a rare symbol, on the other hand, contains a high amount of information about the overall message. It makes perfect sense to me. Or does it?

With this said, let’s jump to the mutual information between two variables .

This quantity measures the mutual dependence between and .

It is given by

, which basically translates into “how much information from knowing , reduces the uncertainty about ?”

In fact, if and are independent, then and . This too makes perfect sense to me. There are some properties that make the link between mutual information and entropy even stronger. I will list a few:

1. , means that the mutual information between and itself is its entropy. Once is known, the amount of uncertainty about itself is indeed its entropy

2. with , one means that the amount of uncertainty about , that remains after is known is .

3. More generally, , which means that a variable contains at least as much information as the one provided by any other variable.

4. Finally, , which means that uncertainty decreases as other variables are known (namely, as the system goes towards a fixed certain state).

One elegant interpretation of entropy in statistics is the Kullback-Leibler divergence

Let’s revisit these concepts in statistics now. One of the most explicative interpretations of mutual information is the one that recalls the Kullback-Leibler distance between distributions.

It represents mutual information as

that I find elegant and amazing at the same time. Let me just add this reconstruction:

that means the more differs from , the higher the amount of “information gain”.

Cool uh?

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