Modeling Reasoning with Bayesian Networks Adnan Darwiche Hardcover 9780521884389

Description: Modeling and Reasoning with Bayesian NetworksAdnan Darwiche Cambridge University Press Hardcover Unused and unread, minor cosmetic imperfections such as scuffing or minor creasing. Stamped 'damaged' by publisher to a non-text page. EAN: 9780521884389 Published: 28/05/2009 Language: English This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer. 1. Introduction 2. Propositional logic 3. Probability calculus 4. Bayesian networks 5. Building Bayesian networks 6. Inference by variable elimination 7. Inference by factor elimination 8. Inference by conditioning 9. Models for graph decomposition 10. Most likely instantiations 11. The complexity of probabilistic inference 12. Compiling Bayesian networks 13. Inference with local structure 14. Approximate inference by belief propagation 15. Approximate inference by stochastic sampling 16. Sensitivity analysis 17. Learning the maximum likelihood approach 18. Learning the Bayesian approach Appendix A notation Appendix B concepts from information theory Appendix C fixed point iterative methods Appendix D constrained optimization. DispatchIn stock here - same-day dispatch from England. My SKU: 3256856RefundsNo-hassle refunds are always available if your book is not as expected.Terms and Conditions of SaleSorry - no collections. All sales are subject to extended Terms and Conditions of Sale as well as the Return Policy and Payment Instructions. Visit my eBay Store for details andmany more books. Template layout and design, "JNC Academic Books", "needbooks", Copyright © JNC INC. Designated trademarks, layouts and brands are the property of their respective owners. All Rights Reserved.

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