| Project/Area Number |
22500206
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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 | University of Miyazaki |
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Principal Investigator |
DATE Akira 宮崎大学, 工学部, 准教授 (60322707)
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| Co-Investigator(Renkei-kenkyūsha) |
KURATA Koji 琉球大学, 工学部, 教授 (40170071)
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| Project Period (FY) |
2010-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2011: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2010: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
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| Keywords | 確率的情報処理 / 確率モデル / ベイズ推論 / 確率推論 / 事後確率 / 隠れマルコフモデル / マルコフ確率場 / 事後確率分布 / パターン認識 / グラフィカルモデル / 数理モデル / パターン情報処理 |
|---|
| Research Abstract |
Probabilistic generative models work in many applications of image analysis and speech recognition. In general, there is an observation vector y and a state vector x, and a joint dependency structure among them. The object of interest is, given y, the most meaningful configuration x and the posterior distribution Pr(x|y). In practice, the structure of the posterior distribution Pr(x|y) is hard to know, and it might have a peculiar structure, especially when x is high dimensional vector. In this project, we developed a method which finds a meaningful estimator by generating a large number of samples from posterior distribution. We performed computer experiments of simple hidden Markov models in which the various functions of the posterior probability distribution is obtainable. Based on the experiments, the effectiveness of the method was discussed.
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