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

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 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 boosting the effectiveness of graphical types together with modeling, reasoning, version choice, logic-probability family members, and causality. The exploration of methodologies is complemented discussions of useful issues for making use of graphical types in actual global settings, protecting 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

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This metric was designated as the Bayesian Dirichlet equivalence (BDe) score metric. As Buntine (1991) described, αijk = α/(ri qi ) is regarded as a special case of the BDe metric. Heckerman et al. (1995) called this special case “BDeu”. Actually, αijk = α/(ri qi ) does not mean “uniform prior,” but “is the same value of all hyperparameters for a variable”. ” Score-based learning Bayesian networks are hindered by heavy computational costs. However, a conditional independence (CI) based approach is known to relax this problem and to extend the available learning network size.

Thus, searching more reasonable suboptimal parameters is a possible approach to learn better BN parameters. In this paper, we propose to visualize suboptimal parameters with parallel coordinate system and propose a Spatially Maximum a Posteriori (SMAP) method. Experimental results reveal that the proposed method outperforms most of the existing parameter learning methods. Keywords: Bayesian Networks · Parameter learning Convex optimization · Linear programming 1 · Small data set · Introduction Bayesian Network (BN) is a type of directed acyclic graph (DAG) with parameters, which is the combination of probability theory and graphical model theory [1].

Generally, to construct a BN, relevant data is required and the number is decided by complexity of the problem to be solved. When sufficient data is available, constructing a good BN model from training data can be accomplished by traditional methods, like Maximum Likelihood [8] for parameter learning. Unfortunately, for domains like earthquake prediction and new-emerging disease diagnosis, collecting sufficient data is tough. In that situation, domain knowledge is often merged into modelling process of the network as supplement information.

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