In recent years probabilistic graphical models, especially Bayesian networks and decision graphs, have experienced significant theoretical development within areas such as artificial intelligence and statistics. This carefully edited monograph is a compendium of the most recent advances in the area of probabilistic graphical models such as decision graphs, learning from data and inference. It presents a survey of the state of the art of specific topics of recent interest of Bayesian Networks, including approximate propagation, abductive inferences, decision graphs, and applications of influence. In addition, Advances in Bayesian Networks presents a careful selection of applications of probabilistic graphical models to various fields such as speech recognition, meteorology or information retrieval.
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Hypercausality, Randomisation Local and Global Independence.- Interface
Verification for Multiagent Probabilistic Inference.- Optimal TimeSpace
Tradeoff In Probabilistic Inference.- Hierarchical Junction Trees.-
Algorithms for Approximate Probability Propagation in Bayesian Networks.-
Abductive Inference in Bayesian Networks: A Review.- Causal Models, Value of
Intervention, and Search for Opportunities.- Advances in Decision Graphs.-
Real-World Applications of Influence Diagrams.- Learning Bayesian Networks by
Floating Search Methods.- A Graphical Meta-Model for Reasoning about Bayesian
Network Structure.- Restricted Bayesian Network Structure Learning.- Scaled
Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the
EM Algorithm.- Learning Essential Graph Markov Models from Data.- Fast
Propagation Algorithms for Singly Connected Networks and their Applications
to Information Retrieval.- Continuous Speech Recognition Using Dynamic
Bayesian Networks: A Fast Decoding Algorithm.- Applications of Bayesian
Networks in Meteorology.
