Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
Motivation Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular...
Guido Sanguinetti, Neil D. Lawrence, Magnus Rattra...
We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
d Abstract) Jane Hillston Laboratory for Foundations of Computer Science, The University of Edinburgh, Scotland Quantitative Analysis Stochastic process algebras extend classical p...
As power system loading increases, larger blackouts due to cascading outages become more likely. We investigate a critical loading at which the average size of blackouts increases...
Ian Dobson, Jie Chen, Jim Thorp, Benjamin A. Carre...