Asymptotic theory for iteration required Monte Carlo algorithms
| Project/Area Number |
22740055
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
General mathematics (including Probability theory/Statistical mathematics)
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| Research Institution | Osaka University (2011)
The University of Tokyo (2010) |
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Principal Investigator |
KAMATANI Kengo 大阪大学, 大学院・基礎工学研究科, 助教 (00569767)
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| Project Period (FY) |
2010 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| Budget Amount *help |
¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2011: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2010: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 統計数学 / モンテカルロ法 / 漸近展開 / マルコフ連鎖モンテカルロ法 / エルゴード性 / 隠れマルコフモデル / カテゴリカルデータ / 次世代シーケンサー / 局所漸近正規性 / ベイズ統計学 / マルコフ連鎖 / 拡散過程 / 混合モデル / ロジスティク回帰 |
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| Research Abstract |
The aim of my research was to study the behaviors of Iteration Required Monte Carlo(ITRMC) algorithm, in particular, Markov chain Monte Carlo. I introduced the notion of degeneracy and weak consistency for those algorithms. I applied the result to mixture model and categorical data model and showed degeneracy of ITRMC algorithms for them. Taking advantage of our approach, I proposed efficient Monte Carlo methods which have better convergence property.
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Report
(3 results)
Research Products
(17 results)
