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Dynamic Bayesian Networks: Representation, Inference and Learning
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Exploiting parameter domain knowledge for learning in Bayesian networks
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Switching Kalman Filters
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Modelling gene expression data using dynamic bayesian networks
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Graphical models and automatic speech recognition
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Logic, Knowledge Representation and Bayesian Decision Theory
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Graphical Models And Variational Approximation
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Proposed design for gR, a graphical models toolkit for R
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Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains
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Conditioning Graphs: Practical Structures for Inference in Bayesian Networks
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Learning Probabilities for Noisy First-Order Rules
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Just Enough Die-Level Test: Optimizing IC Test via Machine Learning and Decision Theory
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Bayesian AI Tutorial
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