Presentation | 2008-03-12 Gaussian Graphical Model on Scale-Free Network Takafumi USUI, Muneki YASUDA, Kazuyuki TANAKA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We consider probabilistic inferences formulated by using Gaussian graphical models on scale free networks. We can derive the statistical performance for the probabilistic inference system by using the multi-dimensional Gaussian integral formulas. We extend the framework of the probabilistic inference to the hyperparameter estimation based on the EM algorithm. We calculate the statistical trajectories for the EM algorithms in the present probabilistic inference systems and compare the results for the scale free networks with the ones for regular graphs and random networks. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | probabilistic inference / complex network / statistical learning / bayesian network / probabilistic information processing |
Paper # | NC2007-112 |
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Committee | NC |
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Conference Date | 2008/3/5(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Gaussian Graphical Model on Scale-Free Network |
Sub Title (in English) | |
Keyword(1) | probabilistic inference |
Keyword(2) | complex network |
Keyword(3) | statistical learning |
Keyword(4) | bayesian network |
Keyword(5) | probabilistic information processing |
1st Author's Name | Takafumi USUI |
1st Author's Affiliation | Graduate School of Information Sciences, Tohoku University() |
2nd Author's Name | Muneki YASUDA |
2nd Author's Affiliation | Graduate School of Information Sciences, Tohoku University |
3rd Author's Name | Kazuyuki TANAKA |
3rd Author's Affiliation | Graduate School of Information Sciences, Tohoku University |
Date | 2008-03-12 |
Paper # | NC2007-112 |
Volume (vol) | vol.107 |
Number (no) | 542 |
Page | pp.pp.- |
#Pages | 5 |
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