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NN
1997
Springer
174views Neural Networks» more  NN 1997»
14 years 22 days ago
Learning Dynamic Bayesian Networks
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...
Zoubin Ghahramani
KI
2010
Springer
13 years 6 months ago
Situation-Specific Intention Recognition for Human-Robot Cooperation
Recognizing human intentions is part of the decision process in many technical devices. In order to achieve natural interaction, the required estimation quality and the used comput...
Peter Krauthausen, Uwe D. Hanebeck
WSC
2007
13 years 11 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
BIOCOMP
2006
13 years 10 months ago
Dynamic Bayesian Network (DBN) with Structure Expectation Maximization (SEM) for Modeling of Gene Network from Time Series Gene
Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectatio...
Yu Zhang, Zhidong Deng, Hongshan Jiang, Peifa Jia
ICRA
2008
IEEE
150views Robotics» more  ICRA 2008»
14 years 3 months ago
Rigorously Bayesian range finder sensor model for dynamic environments
— This paper proposes and experimentally validates a Bayesian network model of a range finder adapted to dynamic environments. The modeling rigorously explains all model assumpt...
Tinne De Laet, Joris De Schutter, Herman Bruyninck...