Explores the Mills ratio, comparing tail behavior of Student t and normal distributions to illustrate fat-tailed vs. thin-tailed distributions.
Explains the Dirichlet distribution as a multivariate extension of the Beta distribution, with applications in Bayesian statistics and regression models.
A non-expert's humorous exploration of diffusion models as a method for sampling from arbitrary probability distributions, touching on measure transport.
Explores the logit-normal distribution, its mathematical properties, and its surprising role in statistical models like logistic regression.
Explores kernel methods and L1 distances for statistical two-sample testing, comparing their effectiveness in determining if datasets come from the same distribution.
Explains statistical methods for testing random number generators in R, focusing on hypothesis testing and probability bounds.
