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| Title | Asymptotics of Bayesian estimation for nested models under misspecification (in Japanese) |
|---|---|
| Authors | Nozomi Miya 、Tota Suko 、Goki Yasuda 、Toshiyasu Matsushima |
| Released Year | 2012 |
| Format | International Conference |
| Category | Source coding |
| Jounal Name | Proceedings of the 2012 International Symposium on Information Theory and its Applications |
| Jounal Page | pp.86–90, Honolulu, USA |
| Published Year | 2012 |
| Published Month | 10 |
| Abstract （English） |
We analyze the asymptotic properties of the cumulative logarithmic loss in the decision problem based on the Bayesian principle and explicitly identify the constant terms of the asymptotic equations as in the case of previous studies by Clarke and Barron and Gotoh et al. We assume that the set of models is given that identify a class of parameterized distributions, it has a nested structure and the source distribution is not contained in all the families of parameterized distributions that are identified by each model. The cumulative logarithmic loss is the sum of the logarithmic loss functions for each time decision—, e.g., the redundancy in the universal noiseless source coding. |
| Note （English） |
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