By Joe Suzuki, Maomi Ueno
This quantity constitutes the refereed lawsuits of the second one foreign 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 rigorously reviewed and chosen from a variety of submissions. within the overseas Workshop on complex Methodologies for Bayesian Networks (AMBN), the researchers discover methodologies for boosting the effectiveness of graphical types together with modeling, reasoning, version choice, logic-probability relatives, and causality. The exploration of methodologies is complemented discussions of sensible issues for utilising graphical versions in genuine international settings, overlaying matters like scalability, incremental studying, parallelization, and so on.
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Additional info for Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings
1311 Constraint-Based Learning Bayesian Networks Using Bayes Factor Fig. 7. Average numbers of MEs 27 Fig. 8. Average numbers of EEs Fig. 9. Average numbers of SHDs Fig. 10. Average numbers of MEs. Fig. 11. Average numbers of EEs. In Table 2, the proposed methods are shown to consume more run-time than the traditional MI methods do. In addition, the run-time of the proposed methods increases linearly as the sample size increases. 28 K. Natori et al. Fig. 12. Average numbers of SHDs. From Figs. 7 and 9, Bayes factor with αijk = 1/2 outperforms other methods in many cases.
We will continue to develop an eﬃcient search for ﬁnding the best Bayesian network structures for both criteria. References 1. : Information theory and an extension of the maximum likelihood principle. In: 2nd International Symposium on Information Theory, Budapest, Hungary (1973) 2. : Theory reﬁnement on Bayesian networks. In: Uncertainty in Artiﬁcial Intelligence, Los Angels, CA pp. 52–60 (1991) 3. 2 User/Developer Manual1, University of York (2015) 4. : Large-sample learning of Bayesian networks is NP-hard.
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.
Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings by Joe Suzuki, Maomi Ueno