Modeling and reasoning with Bayesian networks / (Record no. 84220)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03087nam a2200397 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | CR9780511811357 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730160801.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 101021s2009||||enk o ||1 0|eng|d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780511811357 (ebook) |
| 082 00 - CLASSIFICATION NUMBER | |
| Call Number | 519.5/42 |
| 100 1# - AUTHOR NAME | |
| Author | Darwiche, Adnan, |
| 245 10 - TITLE STATEMENT | |
| Title | Modeling and reasoning with Bayesian networks / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 online resource (xii, 548 pages) : |
| 500 ## - GENERAL NOTE | |
| Remark 1 | Title from publisher's bibliographic system (viewed on 05 Oct 2015). |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Propositional logic -- Probability calculus -- Bayesian networks -- Building Bayesian networks -- Inference by variable elimination -- Inference by factor elimination -- Inference by conditioning -- Models for graph decomposition -- Most likely instantiations -- The complexity of probabilistic inference -- Compiling Bayesian networks -- Inference with local structure -- Approximate inference by belief propagation -- Approximate inference by stochastic sampling -- Sensitivity analysis -- Learning : the maximum likelihood approach -- Learning : the Bayesian approach. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Graphic methods. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1017/CBO9780511811357 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cambridge : |
| -- | Cambridge University Press, |
| -- | 2009. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Bayesian statistical decision theory |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Inference. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Probabilities. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Modeling. |
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