In this paper, we present a novel on-line probabilistic generative model that simultaneously deals with both the clustering and the tracking of an unknown number of moving objects...
Building genetic regulatory networks from time series data of gene expression patterns is an important topic in bioinformatics. Probabilistic Boolean networks (PBNs) have been deve...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Abstract. We propose a novel probabilistic method, based on latent variable models, for unsupervised topographic visualisation of dynamically evolving, coherent textual information...
Abstract. In order to increase precision in searching for web pages or web documents, taking the temporal dimension into account is gaining increased interest. A particular problem...