Estimation of the ratio of probability densities has attracted a great deal of attention since it
can be used for addressing various statistical paradigms. A naive approach to density-ratio …

Given samples from distributions $ p $ and $ q $, a two-sample test determines whether to
reject the null hypothesis that $ p= q $, based on the value of a test statistic measuring the …

The objective of change-point detection is to discover abrupt property changes lying behind
time-series data. In this paper, we present a novel statistical change-point detection …

Theory, algorithms, and applications of machine learning techniques to overcome" covariate
shift" non-stationarity. As the power of computing has grown over the past few decades, the …

We propose a method to optimize the representation and distinguishability of samples from
two probability distributions, by maximizing the estimated power of a statistical test based on …

We propose a new scientific application of unsupervised learning techniques to boost our
ability to search for new phenomena in data, by detecting discrepancies between two …

The maximum mean discrepancy (MMD) test could in principle detect any distributional
discrepancy between two datasets. However, it has been shown that the MMD test is …

Mutual information (MI) is useful for detecting statistical independence between random
variables, and it has been successfully applied to solving various machine learning …

Divergence estimators based on direct approximation of density ratios without going through
separate approximation of numerator and denominator densities have been successfully …

This article develops statistics based on the Kullback–Leibler (KL) divergence to monitor
large-scale technical systems. These statistics detect anomalous system behavior by …