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dc.contributor.author林奇煒en_US
dc.contributor.authorLin, Chi-Weien_US
dc.contributor.author盧鴻興en_US
dc.contributor.authorLu, Horng-Shingen_US
dc.date.accessioned2014-12-12T02:41:01Z-
dc.date.available2014-12-12T02:41:01Z-
dc.date.issued2013en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT070152606en_US
dc.identifier.urihttp://hdl.handle.net/11536/74618-
dc.description.abstract貝氏序列分割是一個資料取向的機率密度函數估計的新方法。利用貝氏的方式在資料空間上造出有效的分割,並且在這個分割上建立長條圖。相較於傳統的長條圖,在貝氏序列分割下所造出來的長條圖可以節省很多不必要的切割,並且在高維度達到準確地估計。然而由長條圖所建立的機率密度函數都會是局部常數的不平滑函數; 因此這篇論文探討了基於貝氏序列分割下可行的平滑方法,試著達到更好的機率密度函數估計。zh_TW
dc.description.abstractBayesian 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.en_US
dc.language.isoen_USen_US
dc.subject貝氏序列分割zh_TW
dc.subject長條圖zh_TW
dc.subject密度函數估計zh_TW
dc.subject平滑方法zh_TW
dc.subject樣條曲線zh_TW
dc.subjectBayesian sequential partitioningen_US
dc.subjectDensity estimationen_US
dc.subjectHistogramen_US
dc.subjectSmoothing methoden_US
dc.subjectpenalties splinesen_US
dc.title基於貝氏序列分割的機率密度函數估計zh_TW
dc.titleSmooth Density Estimation based on Bayesian Sequential Partitioningen_US
dc.typeThesisen_US
dc.contributor.department統計學研究所zh_TW
Appears in Collections:Thesis