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| Title: | 使用貝氏 EMM 分析排序資料
Ranking Data Analysis by Bayesian Extended Mallows Model |
| Authors: | 陳俊宇
Chen, Jyun-Yu |
| Contributors: | 翁久幸
Weng, Chiu-Hsing
陳俊宇
Chen, Jyun-Yu |
| Keywords: | 排序聚合
貝氏估計
網路搜尋引擎
Extend Mallows Model
Mallows Model |
| Date: | 2024 |
| Issue Date: | 2024-08-05 13:59:40 (UTC+8) |
| Abstract: | 排序是常見的資料型態，利用排序聚合方法 (rank aggregation) 集結數個排序，以得到一個更可靠的聚合排序更是重要的課題。本論文建立在Li et al. (2020) 所提出的 Extended Mallows Model (EMM)，一種用於分析排序資料的機率模型，EMM 在聚合排序的同時還可以衡量不同排序的差異。本文提出貝氏方法估計 EMM 的參數，發現貝氏估計相對於 Expectation Conditional Maximization (ECM) 演算法 (Meng and Rubin, 1993) 所迭代出的最大概似估計對起始值較不敏感，且可以藉由 MCMC 採樣值推論參數的不確定性。我們也嘗試了不同數量的起始值以觀察 ECM 的表現，發現當起始值數量增加，某些參數的最大概似估計的 MSE 會降低，但估計的排序與真實排序的 Kendall’s tau 距離卻略微上升。在實際資料分析的部分，本文比較不同網路搜尋引擎的差異，並聚合不同的
搜尋結果。我們也分析 NBA Team Ranking 資料集 (Deng et al., 2014)，並與其他方法做比較。 |
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| Description: | 碩士
國立政治大學
統計學系
111354016 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111354016 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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