Advances in Bayesian Networks by Alireza Daneshkhah, Jim. Q. Smith (auth.), Dr. José A.

By Alireza Daneshkhah, Jim. Q. Smith (auth.), Dr. José A. Gámez, Professor Serafín Moral, Dr. Antonio Salmerón (eds.)

lately probabilistic graphical versions, particularly Bayesian networks and choice graphs, have skilled major theoretical improvement inside parts similar to man made Intelligence and records. This conscientiously edited monograph is a compendium of the newest advances within the quarter of probabilistic graphical versions corresponding to choice graphs, studying from info and inference. It provides a survey of the state-of-the-art of particular subject matters of contemporary curiosity of Bayesian Networks, together with approximate propagation, abductive inferences, determination graphs, and functions of effect. moreover, "Advances in Bayesian Networks" provides a cautious collection of functions of probabilistic graphical types to numerous fields comparable to speech reputation, meteorology or info retrieval

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Extra resources for Advances in Bayesian Networks

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3 as a systematic search problem. Time-space tradeoff curves for several published Bayesian networks are then presented in Sect. 4. Finally, in Sect. 5, we provide some concluding remarks. 2 Any-Space Inference The RC algorithm for exact inference in Bayesian networks works by using conditioning and case analysis to decompose a network into smaller subnetworks that are solved independently and recursively. The algorithm is driven by a structure known as a decomposition tree (dtree), which controls the decomposition process at each level of the recursion.

D. Koller and A. Pfeffer. 1997. Object-oriented Bayesian networks. In D. P. Shenoy, editors, Proc. 13th Conf. on Uncertainty in Artificial Intelligence, pages 302-313, Providence, Rhode Island. 7. R. D. Erman. 1980. Distributed interpretation: a model and experiment. IEEE Trans. on Computers, C-29(12):1144-1163. 8. L. R. Johnson. 1989. DATMS: a framework for distributed assumption based reasoning. In L. N. Huhns, editors, Distributed Artificial Intelligence II, pages 293-317. Pitman. 9. P. McBurney and S.

C) Otherwise, if a message mi is 1, then A 0 compares 1ri(x) with 7rj(x) for each downstream adjacent agent Aj. If j is found such that 1ri (x) ~ 7rj(x), Ao sends 0. If not found, Ao sends 1. (d) Otherwise, continue. 3. Ao compares each 1ri(x) with the parents 1fc(x) shared with Ac· If there exists i such that 1fc(x) ~ 1ri(x), then A 0 sends 1 to Ac. Otherwise, A 0 sends -1. The following theorem establishes that d-sepnode condition can be verified correctly by agent cooperation through CollectPublicParentlnfo.

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