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» Explaining inferences in Bayesian networks
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ACL
2012
11 years 10 months ago
Learning to "Read Between the Lines" using Bayesian Logic Programs
Most information extraction (IE) systems identify facts that are explicitly stated in text. However, in natural language, some facts are implicit, and identifying them requires â€...
Sindhu Raghavan, Raymond J. Mooney, Hyeonseo Ku
IDA
2003
Springer
14 years 25 days ago
Learning Dynamic Bayesian Networks from Multivariate Time Series with Changing Dependencies
Abstract. Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any mode...
Allan Tucker, Xiaohui Liu
AAAI
2008
13 years 10 months ago
A General Framework for Generating Multivariate Explanations in Bayesian Networks
Many existing explanation methods in Bayesian networks, such as Maximum a Posteriori (MAP) assignment and Most Probable Explanation (MPE), generate complete assignments for target...
Changhe Yuan, Tsai-Ching Lu
IEEEARES
2006
IEEE
14 years 1 months ago
Modeling Dependable Systems using Hybrid Bayesian Networks
A hybrid Bayesian Network (BN) is one that incorporates both discrete and continuous nodes. In our extensive applications of BNs for system dependability assessment the models are...
Martin Neil, Manesh Tailor, Norman E. Fenton, Davi...
CORR
2012
Springer
210views Education» more  CORR 2012»
12 years 3 months ago
Fast MCMC sampling for Markov jump processes and continuous time Bayesian networks
Markov jump processes and continuous time Bayesian networks are important classes of continuous time dynamical systems. In this paper, we tackle the problem of inferring unobserve...
Vinayak Rao, Yee Whye Teh