2013 Fiscal Year Final Research Report
Generation of fundamental design theory of Bayesian ad-hoc network systems based on Markov random fields
| Project/Area Number |
24650115
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Single-year Grants |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Tohoku University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
WAIZUMI Yuji 東北大学, 大学院情報科学研究科, 准教授 (90333872)
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| Co-Investigator(Renkei-kenkyūsha) |
YASUDA Muneki 山形大学, 学院理工学研究科, 准教授 (20532774)
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| Project Period (FY) |
2012-04-01 – 2014-03-31
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| Keywords | 確率的情報処理 / 統計的機械学習理論 / マルコフ確率場 / 確率伝播法 / アドホックネットワーク |
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| Research Abstract |
We have investigated some mathematical structures of computational models by using Markov random fields and loopy belief propagations for designs of probabilistic ad-hoc network systems. Although Markov random fields are useful for probabilistic image processing systems, ad-hoc network systems are constructed in terms of random graphs which have large number of degree at each node. In the stand point of view, we have formulated advanced computational models on such random graphs by using loopy belief propagations and have found the existence of several singular points in some statistical quantities, which are regarded as functions of hyperparameters, in such computational models. Moreover, we have succeeded in applying our design strategies to some prediction systems of the traffic density at each road in the city traffic. This is also our results which could not be expected at the application of the present research project.
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