This is the famous Monty Hall Problem. What if I tell you that by learning just the basics of probability, you have a higher chance of winning this contest and going home with a brand new Audi?

Statistics and Probability are subjects which are widely overlooked when it comes to Machine Learning. A lot many people tend to ignore them, because they come off as being difficult and maybe not as cool as Machine Learning. But in order to understand and grasp the core concepts behind some of the most prominently used Machine Learning algorithms, it is important that one is at least familiar with the basics of Statistics and Probability. The aim of this article is to give you a valuable introduction to Probability and its various types. Along with that, we also need to figure out the Monty Hall problem, so let’s go over a few important things.

Probability

Probability, as the name suggests, is nothing but an estimate of how likely an event might take place. Also known as Marginal Probability, it is simply a number that reflects the likelihood that an event will take place. It could be a number between 0 and 1 or it could be expressed as a percentage value. Let us take it step by step.

Experiment

We will define an Experiment within the context of Probability Theory — a branch of mathematics dealing particularly with probability. An Experiment is defined as a procedure which, although can be repeated infinite number of times, still has a well-defined set of possible outcomes.

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