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PKDD
2009
Springer
146views Data Mining» more  PKDD 2009»
14 years 1 months ago
Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks
Monitoring the variables of real world dynamic systems is a difficult task due to their inherent complexity and uncertainty. Particle Filters (PF) perform that task, yielding prob...
Eva Besada-Portas, Sergey M. Plis, Jesús Ma...
CVPR
2004
IEEE
14 years 29 days ago
Motion Without Correspondence from Tomographic Projections by Bayesian Inversion Theory
In conventional tomography, the interior of an object is reconstructed from tomographic projections such as X-ray or electron microscope images. All the current reconstruction met...
Sami S. Brandt, Ville Kolehmainen
AAAI
2008
13 years 11 months ago
Reducing Particle Filtering Complexity for 3D Motion Capture using Dynamic Bayesian Networks
Particle filtering algorithms can be used for the monitoring of dynamic systems with continuous state variables and without any constraints on the form of the probability distribu...
Cédric Rose, Jamal Saboune, François...
CCS
2008
ACM
13 years 11 months ago
Measuring network security using dynamic bayesian network
Given the increasing dependence of our societies on networked information systems, the overall security of these systems should be measured and improved. Existing security metrics...
Marcel Frigault, Lingyu Wang, Anoop Singhal, Sushi...
UAI
1996
13 years 10 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon