Probabilistic reasoning in intelligent systems : networks of plausible inference Export

@book{citeulike:235332, abstract = {{<p><I>Probabilistic Reasoning in Intelligent Systems</I> is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.<br><p>The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.</p><br><p><I>Probabilistic Reasoning in Intelligent Systems</I> will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.</p>}}, author = {Pearl, Judea}, citeulike-article-id = {235332}, citeulike-linkout-0 = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&amp;path=ASIN/1558604790}, citeulike-linkout-1 = {http://www.amazon.de/exec/obidos/redirect?tag=citeulike01-21&amp;path=ASIN/1558604790}, citeulike-linkout-10 = {http://www.worldcat.org/oclc/40272569}, citeulike-linkout-2 = {http://www.amazon.fr/exec/obidos/redirect?tag=citeulike06-21&amp;path=ASIN/1558604790}, citeulike-linkout-3 = {http://www.amazon.jp/exec/obidos/ASIN/1558604790}, citeulike-linkout-4 = {http://www.amazon.co.uk/exec/obidos/ASIN/1558604790/citeulike00-21}, citeulike-linkout-5 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/1558604790}, citeulike-linkout-6 = {http://www.worldcat.org/isbn/1558604790}, citeulike-linkout-7 = {http://books.google.com/books?vid=ISBN1558604790}, citeulike-linkout-8 = {http://www.amazon.com/gp/search?keywords=1558604790&index=books&linkCode=qs}, citeulike-linkout-9 = {http://www.librarything.com/isbn/1558604790}, day = {01}, howpublished = {Paperback}, isbn = {1558604790}, keywords = {1988, bayesian, bayesian-networks, books, borrowed-books, causality, probability-theory}, month = {September}, posted-at = {2008-06-13 18:43:27}, priority = {2}, publisher = {Morgan Kaufmann Publishers}, title = {Probabilistic reasoning in intelligent systems : networks of plausible inference}, url = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20&path=ASIN/1558604790}, year = {1997} }

TY - BOOK ID - citeulike:235332 L3 - citeulike-article-id:235332 N2 - {<p><I>Probabilistic Reasoning in Intelligent Systems</I> is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.<br><p>The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.</p><br><p><I>Probabilistic Reasoning in Intelligent Systems</I> will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.</p>} SN - 1558604790 TI - Probabilistic reasoning in intelligent systems : networks of plausible inference PB - Morgan Kaufmann Publishers KW - 1988 KW - bayesian KW - bayesian-networks KW - books KW - borrowed-books KW - causality KW - probability-theory AU - Pearl, Judea PY - 1997/09/01/ UR - http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&amp;path=ASIN/1558604790 ER -