Smooth Density Estimation based on Bayesian Sequential Partitioning
碩士 === 國立交通大學 === 統計學研究所 === 102 === Bayesian Sequential Partitioning (BSP) is a data-driven method on density estimation which partitions the sample space by the Bayesian approach and then constructs the histogram (Lu, Jiang and Wong, 2013). It can reduce unnecessary cuts in the construction of his...
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| Format: | Others |
| Language: | en_US |
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2014
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| Online Access: | http://ndltd.ncl.edu.tw/handle/h9vy3a |
| Summary: | 碩士 === 國立交通大學 === 統計學研究所 === 102 === Bayesian Sequential Partitioning (BSP) is a data-driven method on density estimation which partitions the sample space by the Bayesian approach and then constructs the histogram (Lu, Jiang and Wong, 2013). It can reduce unnecessary cuts in the construction of histogram and perform accurate estimation in high dimension. However, the estimated density using BSP is not smooth since it provides the density estimation by a histogram. Therefore, this thesis discusses possible smoothing methods based on BSP to estimate smooth densities.
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