In this post we compare the performance of our Python implementation for two popular methods to simulate samples from a pair of independent normal random variables: Box-Muller and Marsaglia-Bray.
Simulating Normal Random Variables
Two simple ways to simulate a pair of independent standard normal distributions with Python code.
– Box-Muller Transform
– Marsaglia-Bray Transform
JP Morgan Challenge
On July 4, 2019 I participated on the JP Morgan Corporate Challenge for the first time. A couple of days after the race the results were online and I could not help but taking a look at the data. Here are the highlights!
Fertility Rate in Mexico 1950-2015
This post contains a Bokeh app showing the rapid change in Fertility Rate that took place in Mexico during the last 50 years. The idea is to illustrate how simple […]
Testing for Normality
Simple Python function to test for Normality. Visual Tools and Hypothesis Tests.
Anatomy of a Box Plot
This post is dedicate to one of the most popular graphs in data visualisation: the Box Plot, a simple tool which was introduced 40 years ago but remains in fashion.
Brownian Motion via Random Walks
The idea of this post is to show how Donsker’s Theorem (also known as Donsker’s Invariance Principle, or the Functional Central Limit Theorem) allows us to simulate paths of the one-dimensional standard Brownian motion using different kinds of random walks.
Brief History of the Central Limit Theorem
A very brief recollection of the history of the Central Limit Theorem: 162 years from Moivre to Feller in a page. If you are interested in reading more about the history […]
Loose Stones
In the article “How can I be more productive? By thinking like an economist, you could do twice as much work in half the time. Sort of” feature in the 1843 […]
Comparison of Two Populations
A short compilation of visual methods for comparing two samples.