We can break down the Mutual Information formula into the following parts:

The x, X and y, Y

x and y are the individual observations/values that we see in our data. X and Y are just the set of these individual values. A good example would be as follows:

Discrete/Binary observation of umbrella-wielding and weather

And assuming we have 5 days of observations of Bob in this exact sequence:

Discrete/Binary observation of umbrella-wielding and weather over 5 days

Individual/Marginal Probability

These are just the simple probability of observing a particular x or y in their respective sets of possible X and Y values.

Take x = 1 as an example: the probability is simply 0.4 (Bob carried an umbrella 2 out of 5 days of his vacation).

Joint Probability

This is the probability of observing a particular x and y from the joint probability of (X, Y). The joint probability (X, Y) is simply just the set of paired observations. We pair them up according to their index.

In our case with Bob, we pair the observations up based on which day they occurred.

You may be tempted to jump to a conclusion after looking at the pairs:

Since there are equal-value pairs occurring 80% of the time, it clearly means that people carry umbrellas BECAUSE it is raining!

Well I’m here to play the devil’s advocate and say that that may just be a freakish coincidence:

If the chance of rain is very low in Singapore, and, independently, the likelihood of Bob carrying umbrella is also equally low (because he hates holding extra stuff), can you see that the odds of having (0,0) paired observations will be very high naturally?

So what can we do to prove that these paired observations are not by coincidence?

Joint Versus Individual Probabilities

We can take the ratio of both probabilities to give us a clue on the “extent of coincidence”.

In the denominator, we take the product of both individual probabilities of a particular x and particular y occurring. Why did we do so?

Peering into the humble coin toss

Recall the first lesson you took in statistics class: calculating the probability of getting 2 heads in 2 tosses of a fair coin.

  • 1st Toss [ p(x) ]: There’s a 50% chance of getting heads
  • 2nd Toss [ p(y) ]: There’s still a 50% chance of getting heads, since the outcome is independent of what happened in the 1st toss
  • The above 2 tosses make up your individual probabilities
  • Therefore, the theoretical probability of getting both heads in 2 independent tosses is 0.5 * 0.5 = 0.25 ( p(x).p(y) )

And if you actually do maybe 100 sets of that double-coin-toss experiment, you’ll likely see that you get the (heads, heads) result 25% of the time. The 100 sets of experiment is actually your (X, Y) joint probability set!

Hence, when you take the ratio of joint versus combined-individual probabilities, you get a value of 1.

This is actually the real expectation for independent events: the joint probability of a specific pair of values occurring is exactly equal to the product of their individual probabilities! Just like what you were taught in fundamental statistics.

Now imagine that your 100-set experiment yielded (heads, heads) 90% of the time. Surely that can’t be a coincidence…

You expected 25% since you know that they are independent events, yet what was observed is an extreme skew of this expectation.

To put this qualitative feeling into numbers, the ratio of probabilities is now a whopping 3.6 (0.9 / 0.25), essentially 3.6x more frequent than we expected.

As such, we start to think that maybe the coin tosses were not independent. Maybe the result of the 1st toss might actually have some unexplained effect on the 2nd toss. Maybe there is some level of association/dependence between 1st and 2nd toss.

That is what Mutual Information tries to tells us!

Expected Value of Observations

For us to be fair to Bob, we should not just look at the times where his claims are wrong, i.e. calculate the ratio of probabilities of (0,0) and (1,1).

We should also calculate the ratio of probabilities for when his claims are correct, i.e. (0,1) and (1,0).

Thereafter, we can aggregate all 4 scenarios in an expected value method, which just means “taking the average”: aggregate up all ratio of probabilities for each observed pair in (X, Y), then divide it by the number of observations.

That is the purpose of these two summation terms. For continuous variables like my stock market example, we will then use integrals instead.

Logarithm of Ratios

Similar to how we calculate the probability of getting 2 consecutive heads for the coin toss, we are also now calculating the additional probability of seeing the 5 pairs that we observed.

For the coin toss, we calculate by multiplying the probabilities of each toss. For Bob, it’s the same: the probabilities have multiplicative effect on each other to give us the sequence that we observed in the joint set.

With logarithms, we turn multiplicative effects into additive ones:

Converting the ratio of probabilities to their logarithmic variants, we can now simply just calculate the expected value as described above using summation of their logarithms.

Feel free to use log-base 2, e, or 10, it does not matter for the purposes of this article.

Putting It All Together

Formula for Mutual Information for Discrete Observations
Formula for Mutual Information for Discrete Observations

Let’s now prove Bob wrong by calculating the Mutual Information. I will use log-base e (natural logarithm) for my calculations:

So what does the value of 0.223 tell us?

Let’s first assume Bob is right, and that the use of umbrellas are independent from presence of rain:

  • We know that the joint probability will exactly equal the product of the individual probabilities.
  • Therefore, for every x and y permutation, the ratio of probabilities = 1.
  • Taking the logarithm, that equates to 0.
  • Thus, the expected value of all permutations (i.e. Mutual Information) is therefore 0.

But since the Mutual Information score that we calculated is non-zero, we can therefore prove to Bob that he is wrong!

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