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NIPS
2007
13 years 9 months ago
Discovering Weakly-Interacting Factors in a Complex Stochastic Process
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
Charlie Frogner, Avi Pfeffer
UAI
1997
13 years 8 months ago
Sequential Update of Bayesian Network Structure
There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynam...
Nir Friedman, Moisés Goldszmidt
DASFAA
2011
IEEE
270views Database» more  DASFAA 2011»
12 years 11 months ago
AutoBayesian: Developing Bayesian Networks Based on Text Mining
Bayesian network is a widely used tool for data analysis, modeling and decision support in various domains. There is a growing need for techniques and tools which can automatically...
Sandeep Raghuram, Yuni Xia, Jiaqi Ge, Mathew Palak...
ICML
2010
IEEE
13 years 6 months ago
Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing
Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with heterogeneity and non-stationarity in temporal processes. Various ap...
Frank Dondelinger, Sophie Lebre, Dirk Husmeier
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...