Graph clustering has become ubiquitous in the study of relational data sets. We examine two simple algorithms: a new graphical adaptation of the k-medoids algorithm and the Girvan...
We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vector-valued observations usi...
—Triggered by a market relevant application that involves making joint predictions of pedestrian and public transit flows in urban areas, we address the question of how to utili...
Marion Neumann, Kristian Kersting, Zhao Xu, Daniel...
Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with heterogeneity and non-stationarity in temporal processes. Various ap...
In reinforcement learning, it is a common practice to map the state(-action) space to a different one using basis functions. This transformation aims to represent the input data i...