Description
Long-tailed distributions are common in natural and engineered systems; as a result, we encounter extreme values more often than we would expect from a short-tailed distribution. If we are not prepared for these "black swans", they can be disastrous.
But we have statistical tools for identifying long-tailed distributions, estimating their parameters, and making better predictions about rare events.
In this talk, I present evidence of long-tailed distributions in a variety of datasets -- including earthquakes, asteroids, and stock market crashes -- discuss statistical methods for dealing with them, and show implementations using scientific Python libraries.