2015 Fiscal Year Final Research Report
Asymptotic analysis of statistical computation methods for hidden Markov models
| Project/Area Number |
24740062
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
General mathematics (including Probability theory/Statistical mathematics)
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| Research Institution | Osaka University |
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Principal Investigator |
Kamatani Kengo 大阪大学, 基礎工学研究科, 講師 (00569767)
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| Project Period (FY) |
2012-04-01 – 2016-03-31
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| Keywords | ベイズ統計 / モンテカルロ / 漸近理論 / 高次元解析 / 複雑モデル / 大規模データ |
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| Outline of Final Research Achievements |
For the project, I performed analysis on (a) sequential monte carlo methods and (b) markov chain monte carlo (MCMC) methods for high-dimensional complicated models. For (a), we proposed (a-1) efficient strategy for high-dimensional state space models, and (a-2) ensemble strategy with multi-level monte carlo. For (b) we proposed a scale-free MCMC and analysed its performance via high-dimensional asymptotic and ergodicity analysis.
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| Free Research Field |
統計学
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