| Advances in Bayesian Networks 2004 Edition Contributor(s): Gámez, José a. (Editor), Moral, Serafin (Editor), Salmerón Cerdan, Antonio (Editor) |
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ISBN: 3540208763 ISBN-13: 9783540208761 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: February 2004 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Technology & Engineering | Engineering (general) - Mathematics | Applied - Computers | Computer Vision & Pattern Recognition |
| Dewey: 519.542 |
| LCCN: 2004043511 |
| Series: Studies in Fuzziness and Soft Computing |
| Physical Information: 0.81" H x 6.14" W x 9.21" (1.45 lbs) 328 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |