Advanced Methodologies for Bayesian Networks: Second by Joe Suzuki, Maomi Ueno

By Joe Suzuki, Maomi Ueno

This quantity constitutes the refereed lawsuits of the second one overseas Workshop on complex Methodologies for Bayesian Networks, AMBN 2015, held in Yokohama, Japan, in November 2015.

The 18 revised complete papers and six invited abstracts offered have been conscientiously reviewed and chosen from quite a few submissions. within the foreign Workshop on complex Methodologies for Bayesian Networks (AMBN), the researchers discover methodologies for reinforcing the effectiveness of graphical versions together with modeling, reasoning, version choice, logic-probability family members, and causality. The exploration of methodologies is complemented discussions of functional concerns for making use of graphical versions in actual global settings, masking issues like scalability, incremental studying, parallelization, and so on.

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Bayesian network learning with cutting planes. , Pfeffer, A. ), Proceedings of the 27th International Conference on Uncertainty in Artificial Intelligence, pp. 153–160. : A tutorial on learning with Bayesian networks. : Learning Bayesian networks: The combination of knowledge and statistical data. Mach. Learn. : Learning Bayesian network structure using LP relaxations. In: 13th International Conference on Artificial Intelligence and Statistics, vol. 9, pp. : Bayes factors. J. Am. Stat. Assoc. : Exact Bayesian structure discovery in Bayesian networks.

Assoc. : Exact Bayesian structure discovery in Bayesian networks. J. Mach. Learn. Res. : Improving the scalability of optimal Bayesian network learning with external-memory frontier breadth-first branch and bound search. , Pfeffer, A. ), Proceedings of the 27th International Conference on Uncertainty in Artificial Intelligence, pp. 479–488. : Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. : Causality: Models, Reasoning, and Inference. : Learning Bayesian networks with the bnlearn R package.

Skewed distribution. Fig. 5. Uniform distribution. Tsamardinos et al. (2009) proposed the evaluation of the accuracy of the learning structure using the SHD, which is the most efficient metric between the learned and the true structure. The results are depicted in Fig. 6. The results show that our proposed method (#1) produces the best performance. For a strongly skewed distribution (Fig. 3), our proposed method decreases the learning error faster than αijk = 1 as the sample size becomes large. For a skewed distribution (Fig.

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