An introduction to sampling from distributions

In this post, we will discuss how to sample data vectors from a distribution.

You can also find a video introduction sampling here and a video on inverse sampling here

Inverse Sampling

Consider the case of sampling data points from a normal distribution centred around 0 with a standard deviation of 1.

A normal distribution with mean 0 and standard deviation 1

If you are familiar with libraries such as numpy , then you will recall that this can be trivially done by using functions like numpy.random.normal() . But for now, let us assume we do not have access to such a library function. How might one go about achieving this task?

Inversion Sampling

The inversion sampling method is based on the following property :

To prove this theorem, we start out by writing down the CDF(Cumulative Density Function) corresponding to the inverse function.

Given the theorem, we now have an algorithm that generates samples from a given pdf(probability density function) f.

In the next post, we will look into some approximate method of sampling which does not require computing the inverse CDF.