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 complaints of the second one foreign 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 quite a few submissions. within the overseas Workshop on complex Methodologies for Bayesian Networks (AMBN), the researchers discover methodologies for reinforcing the effectiveness of graphical types together with modeling, reasoning, version choice, logic-probability family, and causality. The exploration of methodologies is complemented discussions of useful concerns for using graphical types in genuine international settings, masking 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

Example text

Given a structure g, any two non-adjacent nodes in an autonomous sub-structure g A in g are d-separated given nodes either included in the sub-structure g A or exogenous causes to g A . In this method, starting from a complete undirected graph and proceeding from low to high graph d-separation resolution, the RAI algorithm uncovers the correct pattern of a structure by performing the following sequence of operations. First, all relations between nodes in the structure are checked using the CI test.

Ij1 k ∗ θij2 k ≤ θij3 k ∗ θij4 k , ∀i, k (12) To be noticed, parameter constraints of type (1–7) are all convex, while product synergy constraints are non-convex. 3 Principle of the Proposed Method To illustrate the principle of the proposed method, we take a local network (Fig. 2) of Asian network as the sample BN and explain the parameter learning process. First, a random set of parameters are generated and assumed as the true parameters. Then, small amount of synthetic data is sampled from the network.

Then, parameter orders are constructed from qualitative influence constraints. Finally, the initial parameters are regulated by Isotonic Regression method and ultimately obey all the parameter orders. Isozaki [13] proposed a Minimum Free Energy (MFE) method, which is effective on the basic normalization constraints. First, a minimum free energy function, which consists of Kullback-Leibler divergence and entropy function, is taken as the objective optimization function. Then the minimum free energy function and normalization constraints are combined by Lagrange multipliers.

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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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