Innovations in Bayesian Networks: Theory and Applications by Dawn E. Holmes

By Dawn E. Holmes

Bayesian networks at present supply probably the most swiftly becoming components of study in desktop technological know-how and data. In compiling this quantity we've got introduced jointly contributions from essentially the most prestigious researchers during this box. all the twelve chapters is self-contained.

Both theoreticians and alertness scientists/engineers within the wide zone of synthetic intelligence will locate this quantity beneficial. It additionally presents an invaluable sourcebook for Graduate scholars because it indicates the path of present research.

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Introduction to Bayesian Statistics. Wiley, NY (2004) 4. : Statistical Theory and Methodology. Wiley, NY (1965) 5. : Learning Classification Trees. Statistics and Computing 2 (1992) 6. : Foresight: Its Logical Laws, Its Subjective Source. E. ) Studies in Subjective Probability. Wiley, NY (1964) 7. : On the Probable Error of a Coefficient of Correlation Deduced from a Small Sample. Metron 1 (1921) 8. : On the Mathematical Foundations of Theoretical Statistics. Philos. Trans. Roy. Soc. London, Ser.

Foresight: Its Logical Laws, Its Subjective Source. E. ) Studies in Subjective Probability. Wiley, NY (1964) 7. : On the Probable Error of a Coefficient of Correlation Deduced from a Small Sample. Metron 1 (1921) 8. : On the Mathematical Foundations of Theoretical Statistics. Philos. Trans. Roy. Soc. London, Ser. A 222 (1922) 9. : Inverse Probability. Proceedings of the Cambridge Philosophical Society 26 (1930) 10. : Probabilistic Reasoning in Expert Systems. Wiley, NY (1990) 11. : Learning Bayesian Networks.

23) i=1 where θi is the vector of parameters for the distribution p(xi |pai , θi , S h ), θ s is the vector of parameters (θ 1 , . . 8 In addition, we assume that we have a random sample D = {x1 , . . , xN } from the physical joint probability distribution of X. We refer to an element xl of D as a case. 2, we encode our uncertainty about the parameters θs by defining a (vector-valued) variable Θs , and assessing a prior probability density function p(θ s |S h ). The problem of learning probabilities in a Bayesian network can now be stated simply: Given a random sample D, compute the posterior distribution p(θs |D, S h ).

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