| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Outline of Final Research Achievements |
In this research, we develop a new model selection method for Bayesian estimation based on behavior and relaxation process of MCMC method. An expected result is that the design guidelines of the MCMC method are clarified. The MCMC method used in statistical mechanics and others is often discrete, and in this research the behavior of continuous algorithms is more complicated, and the design guidelines of MCMC method have a spillover effect on a wide range of fields. It is also important to develop a model selection method in a hierarchical probabilistic model. The model selection criteria typified by AIC and BIC assumes asymptotic normality, and when used in a hierarchical model, the result of bias is obtained. Establishing a general-purpose model selection method is a very important task.
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