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

By Joe Suzuki, Maomi Ueno

This quantity constitutes the refereed court cases of the second one overseas Workshop on complicated 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 various submissions. within the overseas Workshop on complicated Methodologies for Bayesian Networks (AMBN), the researchers discover methodologies for boosting the effectiveness of graphical types together with modeling, reasoning, version choice, logic-probability kin, and causality. The exploration of methodologies is complemented discussions of sensible concerns for making use of graphical versions in actual global settings, overlaying matters like scalability, incremental studying, parallelization, and so on.

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Extra info for Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings

Sample text

In Figs. 10, 11, and 12, we depict the experimentally obtained results from using MEs, EEs, and SHDs. Additionally, we show the average of run-time in comparison with the method presented in Table 3. Table 3. Comparison of the average run-time for each CI method in the win95pts network. 7520 Constraint-Based Learning Bayesian Networks Using Bayes Factor 29 From Fig. 10, our proposed method (#1) is shown to be the best. From Fig. 11, our proposed method (#1) tends to be adversely affected by extra edges.

Finally, the optimal parameters are computed as the mean values of the probability distribution, which is inferred by a dynamic discretization junction tree method. Generally, existing methods take the global optimal solution of the constrained optimization problem as the final parameters. However, when the available data is limited, objective function constructed from the data, like likelihood function, will overfit the data. As a result, parameters calculated by the existing methods often fail to approach the true parameters well while some suboptimal parameters approach the true parameters better.

3) If the point we search for lies between point C and point D, then point D is closer to the searched point than point A and point M. Uniformly, the searched point more likely lies between point B and point C (probability 50 %) than that between point A and point B (probability 25 %) and that between point C and point D (probability 25 %). Fig. 5. Principle of the proposed method Based on the above indicated principle, in Fig. 6, under uniform distribution, expected parameters (in pink) have more possibility (50 %) to better approach the true parameters (in red) than the border parameters (in blue) (25 %) and center parameters (in black) (25 %).

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