Sampling the Bernoulli Distribution | Intuition & tutorial using NumPy

How can we generate coin tosses or other binary events from a Bernoulli distribution? Here are the notes https://raw.githubusercontent.com/Ceyron/machine-learning-and-simulation/main/english/essential_pmf_pdf/bernoulli_sampling.pdf

Okay, you have something modelled as a Bernoulli Distribution. For instance, you say the weather can either be bad or good. You know all the parameters, in the Bernoulli case just the probability theta. But how do you sample the distribution? In other words: How do you create datasets that look like they are from this distribution.

Surely, you could just use a sampling method in a high-level library like TensorFlow Probability, but how does it work under the hood? For this we need a source of randomness, which is usually provided by a random number generator of a uniform distribution.

In this video, I want to show the idea of sampling the Bernoulli. Then, we will look at how we could do it in NumPy / Python.

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Timestamps:
00:00 Introduction
00:34 What is sampling at all?
01:37 Example: Intro
01:58 A first (non-random) approach
02:19 A source of randomness
03:35 From Uniform to Bernoulli
04:54 Example (cont.)
05:48 NumPy: Random Generator
06:37 NumPy: Transformation to Bernoulli
07:27 NumPy: Writing a function for sampling
08:17 Outro