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JCB
2006
185views more  JCB 2006»
14 years 10 months ago
Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models
As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we expl...
Andrew Golightly, Darren J. Wilkinson
CVPR
2006
IEEE
16 years 4 days ago
Multi-Target Tracking - Linking Identities using Bayesian Network Inference
Multi-target tracking requires locating the targets and labeling their identities. The latter is a challenge when many targets, with indistinct appearances, frequently occlude one...
Peter Nillius, Josephine Sullivan, Stefan Carlsson
FEGC
2008
84views Biometrics» more  FEGC 2008»
14 years 11 months ago
Structure Inference of Bayesian Networks from Data: A New Approach Based on Generalized Conditional Entropy
We propose a novel algorithm for extracting the structure of a Bayesian network from a dataset. Our approach is based on generalized conditional entropies, a parametric family of e...
Dan A. Simovici, Saaid Baraty
APIN
2008
110views more  APIN 2008»
14 years 10 months ago
Explaining inferences in Bayesian networks
While Bayesian network (BN) can achieve accurate predictions even with erroneous or incomplete evidence, explaining the inferences remains a challenge. Existing approaches fall sh...
Ghim-Eng Yap, Ah-Hwee Tan, HweeHwa Pang
SEMWEB
2005
Springer
15 years 3 months ago
Representing Probabilistic Relations in RDF
Probabilistic inference will be of special importance when one needs to know how much we can say with what all we know given new observations. Bayesian Network is a graphical prob...
Yoshio Fukushige