Abstract. This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities betwee...
Quantum systems are promising candidates of future computing and information processing devices. In a large system, information about the quantum states and processes may be incomp...
This article presents and analyzes algorithms that systematically generate random Bayesian networks of varying difficulty levels, with respect to inference using tree clustering. ...
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the n...
Training principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning. In this paper we present a simple unificatio...