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Richard Neapolitans recent book, Learning Bayesian Networks 200, is an exception, but it is more technically demanding than ours.
Bayesian Artificial Intelligence, in our understanding, is the incorporation of Bayesian inferentialmethods in the development of a software architecture for an Artificial Intelligence (AI).We have chosen to hew close to the theme of using and discovering die elenden victor hugo pdf Bayesian networks both because this is our own main research area and because, important as the other Bayesian learning methods are, we believe the Bayesian network technology is central to the overall.The authors integrate all of Bayesian net technology and learning Bayesian net technologyand apply them both to knowledge engineering.Bayesian methods for concept formation exist (e.g., Chris Wallaces Snob 290 but we do not treat them here.Most texts in this area require somewhat more mathematical sophistication than ours; we presuppose only a basic understanding of algebra and calculus.«Bayesian Artificial Intelligence (Chapman Hall/Crc Computer Science and Data Analysis.Inference in Bayesian Networks, decision Networks, applications of Bayesian Networks.Bayesian Artificial Intelligence, Second Edition (Chapman Hall/CRC corel draw graphic suite 12 activation key Computer Science Data Analysis Repost).We could also have discussed Bayesian methods of classification, polynomial curve fitting, time series modeling, etc.Another distinguishing feature of our text is that we advocate a causal interpretation of Bayesian networks, and we discuss the use of Bayesian networks for causal modeling.Software, exercises, and solutions are available on the authors" Web site.
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