http://www.bibsonomy.org/burst/user/fsteeg/probabilityBibSonomy BuRST Feed for /user/fsteeg/probability2008-08-21T05:23:18+02:00http://www.bibsonomy.org/bibtex/21f1e95f9ec2c9d0160ab2c88b02c6cfc/fsteegfsteeg2007-10-07T10:44:45+02:00bayes probability machine-lerning inference <span style="color:#555555;">J. <a href="http://www.bibsonomy.org/author/Pearl">Pearl</a> </span><em>Proceedings of the American Association of Artificial Intelligence National Conference on AI, </em><em>page133--136. </em><em>Pittsburgh, PA, </em>(<em>1982</em>)Sun Oct 07 10:44:45 CEST 2007Pittsburgh, PAProceedings of the American Association of Artificial Intelligence National Conference on AI133--136Reverend Bayes on inference engines: A distributed hierarchical approach1982bayes probability machine-lerning inference http://www.bibsonomy.org/bibtex/2ce7963b92319d5bcdfb02fb536e4d22b/fsteegfsteeg2007-10-07T10:44:45+02:00bayes probability <span style="color:#555555;">J. H. <a href="http://www.bibsonomy.org/author/Kim">Kim</a> and J. <a href="http://www.bibsonomy.org/author/Pearl">Pearl</a> </span><em>Proceedings of the IJCAI-83, </em><em>page190--193. </em><em>Karlsruhe, Germany, </em>(<em>1983</em>)Sun Oct 07 10:44:45 CEST 2007Karlsruhe, GermanyProceedings of the IJCAI-83190--193A computational model for combined causal and diagnostic reasoning in inference systems1983bayes probability http://www.bibsonomy.org/bibtex/270393415d10f385d15ba816a488a2dff/fsteegfsteeg2007-10-07T10:44:45+02:00probability bayes <span style="color:#555555;">Judea <a href="http://www.bibsonomy.org/author/Pearl">Pearl</a> </span><em>Morgan Kaufmann, </em><em>September1988. </em>Sun Oct 07 10:44:45 CEST 2007PaperbackSeptemberProbabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference1988probability bayes <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>