The change point model (CPM) framework introduced in Hawkins, Qiu, and Kang (2003) and Hawkins and Zamba (2005) provides an effective and computationally efficient method for sequentially detecting multiple changes points in streams of Gaussian random variables, when no information is available regarding the parameters of the distribution in the various segments. It has since been extended in v...

Heavy-tailed distributions naturally occur in many real life problems. Unfortunately, it is typically not possible to compute inference in closed-form in graphical models which involve such heavy-tailed distributions. In this work, we propose a novel simple linear graphical model for independent latent random variables, called linear characteristic model (LCM), defined in the characteristic fun...

Nonlinear transformation is one of the major obstacles to analyzing the properties of multilayer perceptrons. In this letter, we prove that the correlation coefficient between two jointly Gaussian random variables decreases when each of them is transformed under continuous nonlinear transformations, which can be approximated by piecewise linear functions. When the inputs or the weights of a mul...