| Description |
1 online resource. |
| Series |
Frontiers in artificial intelligence and applications ; volume 350
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Frontiers in artificial intelligence and applications ; v. 350.
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| Contents |
Intro -- Title Page -- Abstract -- Acknowledgments -- Contents -- Introduction -- Motivation -- Contributions -- Outline of the Thesis -- Background -- Probabilistic Graphical Models -- Bayesian Networks -- Markov Networks -- Factor graphs -- The belief propagation algorithm -- Inference by Weighted Model Counting -- Propositional satisfiability -- Weighted Model Counting -- Logical structure -- Inference by Weighted Model Integration -- Satisfiability Modulo Theories -- Weighted Model Integration -- Related work -- Modelling and inference -- Learning -- WMI-PA -- Predicate Abstraction |
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Weighted Model Integration, Revisited -- Basic case: WMI Without Atomic Propositions -- General Case: WMI With Atomic Propositions -- Conditional Weight Functions -- From WMI to WMIold and vice versa -- A Case Study -- Modelling a journey with a fixed path -- Modelling a journey under a conditional plan -- Efficiency of the encodings -- Efficient WMI Computation -- The Procedure WMI-AllSMT -- The Procedure WMI-PA -- WMI-PA vs. WMI-AllSMT -- Experiments -- Synthetic Setting -- Strategic Road Network with Fixed Path -- Strategic Road Network with Conditional Plans -- Discussion -- Final remarks |
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MP-MI -- Preliminaries -- Computing MI -- Hybrid inference via MI -- On the inherent hardness of MI -- MP-MI: exact MI inference via message passing -- Propagation scheme -- Amortizing Queries -- Complexity of MP-MI -- Experiments -- Final remarks -- lariat -- Learning WMI distributions -- Learning the support -- Learning the weight function -- Normalization -- Experiments -- Final remarks -- Conclusion |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Machine learning.
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Machine learning. |
| ISBN |
9781643682679 (electronic bk.) |
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1643682679 (electronic bk.) |
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9781643682662 (print) |
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