How To Generate Any Probability Distribution, Part 1: Inverse Transform Sampling

In this post I'd like to briefly describe one of my favourite algorithmic techniques: inverse transform sampling. Despite its scary-sounding name, it is actually quite a simple and very useful procedure for generating random numbers from an arbitrary, known probability distribution — given random numbers drawn from a uniform distribution. For example, if you had …

The Shortest Bayes Classifier Tutorial You’ll Ever Read

The Bayes classifier is one of the simplest machine learning techniques. Yet despite its simplicity, it is one of the most powerful and flexible. Being a classifier, its job is to assign a class to some input. It chooses the most likely class given the input. That is, it chooses the class that maximises $latex P(class | input)$. Being …