New PDF release: Advanced Methodologies for Bayesian Networks: Second

By Joe Suzuki, Maomi Ueno

ISBN-10: 3319283782

ISBN-13: 9783319283784

ISBN-10: 3319283790

ISBN-13: 9783319283791

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 awarded have been rigorously reviewed and chosen from a variety of submissions. within the overseas Workshop on complicated 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 sensible issues for employing graphical types in genuine international settings, masking matters like scalability, incremental studying, parallelization, and so on.

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

Example text

13) and (14) become more trustable and further improve the performance of SMAP method. 5. Final results are shown in Fig. 8. Seen from the experimental results in Fig. 8, we can find that, to achieve any KL divergence, SMAP method requires fewer samples than any other algorithms. Besides, algorithm showing good performance in the first experiment may performs terrible and require much more samples to achieve certain KL divergence. For example, QMAP can learn notably low KL divergence parameters with small data set.

In: Uncertainty in Artificial Intelligence, Acapulco, Mexico, pp. 124–133 (2003) 5. : A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–347 (1992) 6. : Efficient structure learning of bayesian networks using constraints. J. Mach. Learn. Res. 12, 663–689 (2011) 7. : The performance of universal encoding. IEEE Trans. Inf. Theory IT–27(2), 199–207 (1981) 8. : Modeling by shortest data description. Automatica 14, 465–471 (1978) 14 J. Suzuki 9. : A simple approach for finding the globally optimal bayesian network structure.

A severe problem of the CB approach is its lower accuracy of learning than that of a score-based approach. Recently, several CI tests with consistency have been proposed. The main proposal of this study is to apply the CI tests to CB learning Bayesian networks. This method allows learning larger Bayesian networks than the score based approach does. Based on Bayesian theory, this paper addresses a CI test with consistency using Bayes factor. The result shows that Bayes factor with Jeffreys’ prior provides theoretically and empirically best performance.

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Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings by Joe Suzuki, Maomi Ueno


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