Implementing Markov chain quasi-Monte Carlo methods
| Project/Area Number |
26730015
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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 |
Statistical science
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| Research Institution | Ritsumeikan University (2015-2016)
Tokyo Institute of Technology (2014) |
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Principal Investigator |
HARASE Shin 立命館大学, 理工学部, 嘱託講師 (80610576)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2016: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2015: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2014: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
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| Keywords | 擬似乱数 / モンテカルロ法 / 準モンテカルロ法 / マルコフ連鎖モンテカルロ法 / 確率的シミュレーション / 数値積分 / 均等分布次元 |
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| Outline of Final Research Achievements |
(1) We searched for higher-order quasi-Monte Carlo point sets with t-value and WAFOM both small. In addition, we searched for low-WAFOM point sets with the property that the number of points may be increased while retaining the existing points (so-called the extensibility). (2) We developed 64-bit maximally equidistributed F2-linear pseudorandom number generators. (3) We searched for a prototype version of Sobol' point sets with two-dimensional projections even better than the existing ones. We tested our point sets in financial engineering. (4) To construct quasi-Monte Carlo point sets for Markov chain Monte Carlo methods, we explored several kinds of algorithms. In particular, we noticed that short-period Tausworthe generators optimized in terms of regular continued fractions are probably hopeful.
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Report
(4 results)
Research Products
(30 results)
