Learning Bayesian network structure from large-scale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements ...
Abstract. Algorithms for probabilistic inference in Bayesian networks are known to have running times that are worst-case exponential in the size of the network. For networks with ...
Johan Kwisthout, Hans L. Bodlaender, Linda C. van ...
Modern VLSI processing supports a two-dimensional surface for active devices along with multiple stacked layers of interconnect. With the advent of planarization, the number of la...
We propose and analyze an architecture for storage servers in large Video on Demand (VoD) systems. We describe a method for distributing the collection of titles among the levels o...
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...