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JCB
2006
185views more  JCB 2006»
13 years 7 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
NN
1997
Springer
174views Neural Networks» more  NN 1997»
13 years 11 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
JMLR
2002
74views more  JMLR 2002»
13 years 7 months ago
The Representational Power of Discrete Bayesian Networks
One of the most important fundamental properties of Bayesian networks is the representational power, reflecting what kind of functions they can or cannot represent. In this paper,...
Charles X. Ling, Huajie Zhang
BMCBI
2007
172views more  BMCBI 2007»
13 years 7 months ago
Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks
Background: Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly su...
Fulvia Ferrazzi, Paola Sebastiani, Marco Ramoni, R...
KES
2007
Springer
14 years 1 months ago
Credal Networks for Operational Risk Measurement and Management
According to widely accepted guidelines for self-regulation, the capital requirements of a bank should relate to the level of risk with respect to three different categories. Amon...
Alessandro Antonucci, Alberto Piatti, Marco Zaffal...