fictitious data generation using Markov chain Monte Carlo and application to nonlinear information processing
| Project/Area Number |
22500217
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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 |
Sensitivity informatics/Soft computing
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
IBA Yukito 統計数理研究所, モデリング研究系, 准教授 (30213200)
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| Project Period (FY) |
2010 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2012: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2010: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
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| Keywords | 確率的情報処理 / 仮想データ / マルコフ連鎖モンテカルロ法 / マルチカノニカル法 / サロゲーション / プレイメージ生成 / 機械学習 / 数理工学 / 統計数学 / 統計力学 / アルゴリズム / 情報基礎 / モンテカルロ法 |
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| Research Abstract |
A Markov chain Monte Carlo solution for “fictitious data generation” is proposed; it is applied to data surrogation and preimage generation. Surrogation of nonlinear time series is successfully treated by multicanonical Monte Carlo. A preimage problem for drug design corresponding to discrimination of structural diagrams is also studied.
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Report
(4 results)
Research Products
(15 results)
