The following puzzle is a well-known meme in social networks. It is said to have been invented by young Einstein and back in the days I was ambitious enough to solve it by hand (you should try too!).
Yet, even simpler is to use Constraint Programming (CP). An excellent choice for doing that is MiniZinc, a free and open-source constraint modelling language. And the best thing is that you can control it by R! If you want to see how, read on!
Continue reading “Solving Einstein’s Puzzle with Constraint Programming”
Over one billion dollars have been spent in the US to split up big schools into smaller ones because small schools regularly show up in rankings as top performers.
In this post, I will show you why that money was wasted because of a widespread (but not so well known) statistical artifact, so read on!
Continue reading “The Most Dangerous Equation, or Why Small is Not Beautiful!”
One of the most fiercely fought debates in quantitative finance is whether the stock market (or financial markets in general) is (are) efficient, i.e. whether you can find patterns in them that can be profitably used.
If you want to learn about an ingenious method (that is already present in anyone’s computer) to approach that question, read on!
Continue reading “Is the Stock Market Efficient? Let your ZIP Compression Tool give an Answer!”
What is the “opposite” of sampling without replacement? In a classical urn model sampling without replacement means that you don’t replace the ball that you have drawn. Therefore the probability of drawing that colour becomes smaller. How about the opposite, i.e. that the probability becomes bigger? Then you have a so-called Pólya urn model!
Many real-world processes have this self-reinforcing property, e.g. leading to the distribution of wealth or the number of followers on social media. If you want to learn how to simulate such a process with R and encounter some surprising results, read on!
Continue reading “The Pólya Urn Model: A simple Simulation of “The Rich get Richer””