| Project/Area Number |
12640108
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
General mathematics (including Probability theory/Statistical mathematics)
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
MASE Shigeru Tokyo Institute of Technology, Department of Information Sciences, Professor, 大学院・情報理工学研究科, 教授 (70108190)
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| Co-Investigator(Kenkyū-buntansha) |
FUJISAWA Hironori Tokyo Institute of Technology, Department of Information Sciences, Research Associate, 大学院・情報理工学研究科, 助手 (00301177)
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| Project Period (FY) |
2000 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 2001: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2000: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Keywords | Markov random field / Maximum pseudo-likelihood estimator / approximate MLE / MCMC method / Gibbs sampler / canopy height / tropical forest / plant ecology / マルコフ確立場 / 最大擬似尤度推定量 / 近時最尤度推定量 / 空間統計学 / 森林生態系 / ポデーシャル関数 / 最尤推定 / BCIデータ |
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| Research Abstract |
Markov random field models are fitted to 12 years data of canopy heights of neo-tropical forests of Barro Corollado islands, Panama. Canopy heights are binarized, under and over 20m. From various experiments taking account of neighbouring patterns of trees to potential functions, it is verified that Markov random field model can model binary canopy patterns fairly well using 1-bocly, 2-body and 4-body patterns simultaneously. Model parameters are first estimated by the maximum pseudo-likelihood method and they are refined by a MQMC Newton-Raphson refinement clue to Huang and Ogata. Among 12 years data, 8 data shows fairly good fittings and 2 years good fittings. 2 years data cannot show good fittings, probably due to temporal drastic changes of forest systems. But it is seen that temporal unstableness recovers quickly, showing the ecological stableness of the forest. As a result, it is verified that Markov random field model can represent such forest, system. The analysis 43 under the process of submitting to an appropriate journal. Also the head investigator published a first Japanese book on spatial statistics including Markov random field in 2001.
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