%0 Book %1 cheeseman86 %A Cheeseman, P. %B Uncertainty in Artifical Intelligence %C North Holland %D 1986 %I Elsevier Science Publishers B.V. %K inference fuzzy probabilistic reasoning %P 85--102 %T Probabilistic versus Fuzzy Reasoning

@book{cheeseman86, added-at = {2006-03-24T16:34:33.000+0100}, address = {North Holland}, author = {Cheeseman, P.}, biburl = {https://www.bibsonomy.org/bibtex/21bd0732067d5bc6b1e27b9d8b9d7ea34/neilernst}, citeulike-article-id = {111778}, comment = {defends the ability of prob. reasoning to handle everything that fuzzy logic can. one criticism of bayesian reasoning is that you can't always assume that theinitial probability is 50 if you have no extra knowledge; e.g the conditional independence assumption - can concepts be vague? prob as the numerical measure of a person's belief in a particular proposition - "when making decisions with uncertain and incomplete information, it is always necessary to make assumptions" p 91 - how precisely can we model statement probabilities e.g. 90% certain implies a high degree of accuracy - possibility theory confuses the set of possible outcomes with the probability of each outcome other paper: Cheeseman, P. "In Defense of Probability", Proceedings of the Ninth International Joint Conference on Artificial Intelligence, pp. 1002-1009, Morgan Kaufmann, 1985. q334 i56 bsmt}, description = {sdasda}, interhash = {27a1051ca7ac8a0b9039b2086e46fc5c}, intrahash = {1bd0732067d5bc6b1e27b9d8b9d7ea34}, keywords = {inference fuzzy probabilistic reasoning}, pages = {85--102}, priority = {0}, publisher = {Elsevier Science Publishers B.V.}, series = {Uncertainty in Artifical Intelligence}, timestamp = {2006-03-24T16:34:33.000+0100}, title = {Probabilistic versus {F}uzzy {R}easoning}, year = 1986 }