| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2014: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2013: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2012: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2011: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Outline of Final Research Achievements |
In statistical machine learning, it is well known that the appropriate model and prior for a given set of training samples is chosen by minimization of the Bayesian free energy. However, there has been no method to estimate the Bayesian free energy if the posterior distribution can not be approximated by any normal distribution. In this research, we created a new concept, a widely applicable Bayesian information criterion (WBIC), and proved that WBIC has the same asymptotic behavior as the Bayesian free energy, based on the birational geometry. The obtained theorem enables us to choose the optimal model for a given set of training samples, even if the model has hierarchical structures.
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