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CMSB
2009
Springer
13 years 11 months ago
Probabilistic Approximations of Signaling Pathway Dynamics
Systems of ordinary differential equations (ODEs) are often used to model the dynamics of complex biological pathways. We construct a discrete state model as a probabilistic appro...
Bing Liu, P. S. Thiagarajan, David Hsu
WSC
2007
13 years 7 months ago
Analyzing air combat simulation results with dynamic Bayesian networks
In this paper, air combat simulation data is reconstructed into a dynamic Bayesian network. It gives a compact probabilistic model that describes the progress of air combat and al...
Jirka Poropudas, Kai Virtanen
ICDM
2009
IEEE
141views Data Mining» more  ICDM 2009»
13 years 12 months ago
Discovering Excitatory Networks from Discrete Event Streams with Applications to Neuronal Spike Train Analysis
—Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human-compute...
Debprakash Patnaik, Srivatsan Laxman, Naren Ramakr...
MLMI
2004
Springer
13 years 10 months ago
Multistream Dynamic Bayesian Network for Meeting Segmentation
This paper investigates the automatic analysis and segmentation of meetings. A meeting is analysed in terms of individual behaviours and group interactions, in order to decompose e...
Alfred Dielmann, Steve Renals
IJBC
2002
109views more  IJBC 2002»
13 years 5 months ago
Cnn Dynamics represents a Broader Class than PDES
The relationship between Cellular Nonlinear Networks (CNNs) and Partial Differential Equations (PDEs) is investigated. The equivalence between discrete-space CNN models and contin...
Marco Gilli, Tamás Roska, Leon O. Chua, Pie...