Probabilistic World

The Birthday Problem: Python Simulation

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UEFA Nations League semi-final between Netherlands and England (prematch, 06/06/2019), with Python code related to the birthday problem in the background

In my last post, I introduced you to the so-called birthday problem. Namely, the probability of having at least one birthday coincidence in a random group of people. I showed you how to approach the question analytically by deriving a simple formula for calculating this probability.

In this post, I want to show you an alternative way of getting the same probability using a computer simulation with the programming language Python!

This post is part of my series Probability Questions from the Real World.

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The Birthday Problem: Analytic Solution

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UEFA Nations League semi-final between Netherlands and England (prematch, 06/06/2019), with the birthday problem formula in the background

Welcome to the first post from my new series. Here we’re going look at a famous probability question often called the birthday problem. This is actually a more general question related to the probability of at least one coincidence after a fixed number of draws from a discrete uniform distribution.

This post is part of my series Probability Questions from the Real World.

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The Binomial Distribution (and Theorem): Intuitive Understanding

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A skyscraper resembling a binomial distribution

Hi, everyone! And welcome to my post about the binomial distribution! Just like the Bernoulli distribution, this is one of the most commonly used and important discrete probability distributions.

This post is part of my series on discrete probability distributions.

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The Bernoulli Distribution: Intuitive Understanding

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A portrait of Jacob Bernoulli with a 17th century Swiss coin in the background

In today’s post, I’m going to give you intuition about the Bernoulli distribution. This is one of the simplest and yet most famous discrete probability distributions. Not only that, it is the basis of many other more complex distributions.

This post is part of my series on discrete probability distributions.

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