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NN
1997
Springer
174views Neural Networks» more  NN 1997»
13 years 9 months 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
VLSID
2002
IEEE
127views VLSI» more  VLSID 2002»
14 years 5 months ago
Switching Activity Estimation of Large Circuits using Multiple Bayesian Networks
Switching activity estimation is a crucial step in estimating dynamic power consumption in CMOS circuits. In [1], we proposed a new switching probability model based on Bayesian N...
Sanjukta Bhanja, N. Ranganathan
GLOBECOM
2010
IEEE
13 years 3 months ago
A Graphical Framework for Spectrum Modeling and Decision Making in Cognitive Radio Networks
There are many key problems of decision making related to spectrum occupancies in cognitive radio networks. It is known that there exist correlations of spectrum occupancies in tim...
Husheng Li, Robert C. Qiu
ICCV
2009
IEEE
14 years 10 months ago
Modelling Activity Global Temporal Dependencies using Time Delayed Probabilistic Graphical Model
We present a novel approach for detecting global behaviour anomalies in multiple disjoint cameras by learning time delayed dependencies between activities cross camera views. Sp...
Chen Change Loy, Tao Xiang and Shaogang Gong
ICCV
2003
IEEE
14 years 7 months ago
Recognition of Group Activities using Dynamic Probabilistic Networks
Dynamic Probabilistic Networks (DPNs) are exploited for modelling the temporal relationships among a set of different object temporal events in the scene for a coherent and robust...
Shaogang Gong, Tao Xiang