What do you get when you let a monkey randomly smash the buttons on a typewriter?
Hamlet from Shakespeare, of course. And Romeo and Juliet. And every other finite string that is possible.
Don't believe me? Keep reading. ↓
Let's start at the very beginning!
Suppose that I have a coin that, when tossed, has a 1/2 probability of coming up heads and a 1/2 probability of coming up tails.
If I start tossing the coin and tracking the result, what is the probability of 𝑛𝑒𝑣𝑒𝑟 having heads?
To answer this, first, we calculate the probability of no heads in 𝑛 tosses. (That is, the probability of 𝑛 tails.)
Since tosses are independent of each other, we can just multiply the probabilities for each toss together.
By letting 𝑛 to infinity, we obtain that the probability of never tossing heads is zero.
That is, we are going to have heads come up eventually with probability 1.
Instead of coin tosses, we can talk about arbitrary events.
If an event has a nonzero probability and you have infinite attempts, 𝑖𝑡 𝑤𝑖𝑙𝑙 ℎ𝑎𝑝𝑝𝑒𝑛 with probability 1.
Now, let's apply that to our monkey, infinitely typing away at the typewriter.
What is the probability that six random consecutive keystrokes result in the string "Hamlet"?
First, each keystroke matching the right character is 1/(number of keys).
Because the keystrokes are independent, the probability of a given string is the product of the probabilities for each keystroke matching the individual character.
Now, let's calculate the probability that the entire Hamlet play by Shakespeare is typed randomly.
Since the entire play has 194270 characters, and there are 100 possible keys to hit, this probability is extremely small.
Still, it is larger than zero.
Thus, if our monkey keeps typing infinitely, the entire Hamlet play will appear somewhere. (Along with every other finite string you can imagine.)
However, this takes a 𝑣𝑒𝑟𝑦 long time on average.
If the probability of a given string occurring is 𝑝, the expected number of attempts to randomly generate it is 1/𝑝.
So, if 𝑝 is as small as randomly typing the entire Hamlet play, then 1/𝑝 is going to be astronomical.
(If you are not familiar with the concept of expected values, take a look at the simple explanation I posted a while ago.)
It states that given infinite time, a monkey randomly smashing the keys of a typewriter will type any given text.
Next time when you say, "even a monkey can do it", be careful. Monkeys can do a lot.
Recently, I have been thinking about probability a lot.
In fact, I am writing the probability theory chapters of my book, Mathematics of Machine Learning. The early access is just out, where I publish one chapter every week.
No matter the field, you can (almost always) find a small set of mind-numbingly simple ideas making the entire thing work.
In machine learning, the maximum likelihood estimation is one of those.
I'll start with a simple example to illustrate a simple idea.
Pick up a coin and toss it a few times, recording each outcome. The question is, once more, simple: what's the probability of heads?
We can't just immediately assume p = 1/2, that is, a fair coin.
For instance, one side of our coin can be coated with lead, resulting in a bias. To find out, let's perform some statistics! (Rolling up my sleeves, throwing down my gloves.)
The Law of Large Numbers is one of the most frequently misunderstood concepts of probability and statistics.
Just because you lost ten blackjack games in a row, it doesn’t mean that you’ll be more likely to be lucky next time.
What is the law of large numbers, then?
The strength of probability theory lies in its ability to translate complex random phenomena into coin tosses, dice rolls, and other simple experiments.
So, let’s stick with coin tossing. What will the average number of heads be if we toss a coin, say, a thousand times?
To mathematically formalize this question, we’ll need random variables.
Tossing a fair coin is described by the Bernoulli distribution, so let X₁, X₂, … be such independent and identically distributed random variables.
The expected value is one of the most important concepts in probability and statistics.
For instance, all the popular loss functions in machine learning, like cross-entropy, are expected values. However, its definition is far from intuitive.
Here is what's behind the scenes.
It's better to start with an example.
So, let's play a simple game! The rules: I’ll toss a coin, and if it comes up heads, you win $1. However, if it is tails, you lose $2.
Should you even play this game with me? We’ll find out.
After n rounds, your earnings can be calculated by the number of heads times $1 minus the number of tails times $2.
If we divide total earnings by n, we obtain your average earnings per round.
The single biggest argument about statistics: is probability frequentist or Bayesian?
It's neither, and I'll explain why.
Buckle up. Deep-dive explanation incoming.
First, let's look at what is probability.
Probability quantitatively measures the likelihood of events, like rolling six with a dice. It's a number between zero and one. This is independent of interpretation; it’s a rule set in stone.
In the language of probability theory, the events are formalized by sets within an event space.
The event space is also a set, usually denoted by Ω.)
If the sidewalk is wet, is it raining? Not necessarily. Yet, we are inclined to think so. This is a preposterously common logical fallacy called "affirming the consequent".
However, it is not totally wrong. Why? Enter the Bayes theorem.
Propositions of the form "if A, then B" are called implications.
They are written as "A → B", and they form the bulk of our scientific knowledge.
Say, "if X is a closed system, then the entropy of X cannot decrease" is the 2nd law of thermodynamics.
In the implication A → B, the proposition A is called "premise", while B is called the "conclusion".
The premise implies the conclusion, but not the other way around.
If you observe a wet sidewalk, it is not necessarily raining. Someone might have spilled a barrel of water.