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ICML
2000
IEEE
14 years 10 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
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...
BIOINFORMATICS
2006
124views more  BIOINFORMATICS 2006»
13 years 9 months ago
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities
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...
NIPS
2004
13 years 10 months ago
Dynamic Bayesian Networks for Brain-Computer Interfaces
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...
Pradeep Shenoy, Rajesh P. N. Rao
MPC
2010
Springer
181views Mathematics» more  MPC 2010»
14 years 1 months ago
Process Algebras for Collective Dynamics
d Abstract) Jane Hillston Laboratory for Foundations of Computer Science, The University of Edinburgh, Scotland Quantitative Analysis Stochastic process algebras extend classical p...
Jane Hillston
HICSS
2002
IEEE
84views Biometrics» more  HICSS 2002»
14 years 2 months ago
Examining Criticality of Blackouts in Power System Models with Cascading Events
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...