I watched this video,

[youtube https://www.youtube.com/watch?v=IAiNqQi30-Y]

Very interesting.

So, I managed to prove it through some scripting.

https://gist.github.com/allencch/36544a84fdb8159756618290209f1750

And I get some result like this,

[code lang=text] Result: Target: [0, 1] average steps = 3.987 Target: [0, 0] average steps = 5.9505 [/code]

P/S: Wrote a robust flip coin script, which can accept the coin tossing sequence with any length. [here]

Monty Hall paradox

Probability topic is the fundamental concept of the statistics. And machine learning is closely related to statistics. That is why, understand the probability very important if you are doing research, statistics, and machine learning.

Monty Hall is a very interesting problem. It says, if you are given 3 doors to choose. One of them contains a car (which you want), the other two are goats (which you don’t want). After you made your choice, before opening the door, the host will open the door that you didn’t choose yet contains the goat (he knows which door has the goat). Now, if you are given an opportunity to change your choice to another door (which you didn’t choose earlier), are you going to change?

Recently, I feel fervent to learn functional programming, because i) (in my opinion), it will become a trend, and ii) the interpreter can be used as an advanced calculator.

Since I am teaching Statistics, I want to do some calculation of the normal distribution probability.

Before I begin, I need to mention, in order to calculate the normal distribution probability something like P(x < X), this can be done by using a spreadsheet software with NORMDIST() and NORM.INV() for the inverse of the former function.