See how the various parameters impact some of the most well-known distributions .

Sometimes it’s better to actually see the influences of the parameters of distributions. This holds true especially for more complex ones. Hence, I build an app that lets you play around with some of the most ubiquitous distributions. We are starting with a bare minimum of some well-known distributions and will successively add more.

Probability distributions are ubiquitous in the machine learning world and, e.g. at the core of whole fields such as Bayesian learning. So before we start, let us quickly recapture what distributions are and what they are good for in a machine learning context. Here are some descriptions from the net:

In probability theory and statistics, a probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment. (from Wikipedia)

So basically, it is a list of values that a random variable can take on and its corresponding probability. In machine learning, probability densities are often used to connect data and desired outcomes. If the density has parameters, e.g. such as mean and variance in the Gaussian distribution, the overall goal is often to find the best set of parameters to fit the underlying data.

That should be enough and without further ado, have some fun playing with distributions.