Kernel density estimation (KDE) is a versatile nonparametric approach to infer continuous probability distributions from finite samples. By superimposing smooth kernel functions—most commonly Gaussian ...

Scientists using machine learning tools to analyze biomedical data often turn to neural network algorithms, but before these models became popular, another simpler type of machine learning algorithm ...

Are two sets of data genuinely different, or is it because of randomness? This question, known as the two-sample testing problem, becomes notoriously difficult in modern datasets, because they are ...