| Budget Amount *help |
¥43,940,000 (Direct Cost: ¥33,800,000、Indirect Cost: ¥10,140,000)
Fiscal Year 2021: ¥8,840,000 (Direct Cost: ¥6,800,000、Indirect Cost: ¥2,040,000)
Fiscal Year 2020: ¥8,840,000 (Direct Cost: ¥6,800,000、Indirect Cost: ¥2,040,000)
Fiscal Year 2019: ¥8,840,000 (Direct Cost: ¥6,800,000、Indirect Cost: ¥2,040,000)
Fiscal Year 2018: ¥8,840,000 (Direct Cost: ¥6,800,000、Indirect Cost: ¥2,040,000)
Fiscal Year 2017: ¥8,580,000 (Direct Cost: ¥6,600,000、Indirect Cost: ¥1,980,000)
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| Outline of Final Research Achievements |
With the emergence of big data, expectations for "data-driven science" are increasing. However, even if you have a promising data-driven model, you cannot draw any compelling conclusions without assessing the reliability of the model. Based on this recognition of the current situation, in this research, we develop numerical methods to evaluate the reliability of estimated parameters using only available data for large-degree-of-freedom statistical models used in sparse modeling. In particular, we have developed semi-analytical approximation reliability evaluation methods with a small amount of computation by applying the mean-field approximation of statistical mechanics and the replica method, focusing on reducing the computational cost.
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