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» Modeling Dependable Systems using Hybrid Bayesian Networks
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JETAI
1998
110views more  JETAI 1998»
13 years 8 months ago
Independency relationships and learning algorithms for singly connected networks
Graphical structures such as Bayesian networks or Markov networks are very useful tools for representing irrelevance or independency relationships, and they may be used to e cientl...
Luis M. de Campos
PERCOM
2007
ACM
14 years 8 months ago
Structural Learning of Activities from Sparse Datasets
Abstract. A major challenge in pervasive computing is to learn activity patterns, such as bathing and cleaning from sensor data. Typical sensor deployments generate sparse datasets...
Fahd Albinali, Nigel Davies, Adrian Friday
IEEECIT
2010
IEEE
13 years 7 months ago
A Learning Spectrum Hole Prediction Model for Cognitive Radio Systems
—In this paper, we present a new spectrum-hole prediction model for cognitive radio (CR) systems based on the IEEE 802.11 wireless local areas networks. We have also analyzed the...
Zhigang Wen, Chunxiao Fan, Xiaoying Zhang, Yuexin ...
SIGCOMM
1996
ACM
14 years 27 days ago
On the Relevance of Long-Range Dependence in Network Traffic
There is much experimental evidence that network traffic processes exhibit ubiquitous properties of self-similarity and long-range dependence, i.e., of correlations over a wide ran...
Matthias Grossglauser, Jean-Chrysostome Bolot
WSC
1997
13 years 10 months ago
A Hybrid Tool for the Performance Evaluation of NUMA Architectures
We present a system for describing and solving closed queuing network models of the memory access performance of NUMA architectures. The system consists of a model description lan...
James Westall, Robert Geist