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題名 二維條件分配相容性問題之新解法
A new approach to solve the compatibility issues for two-dimensional conditional distributions
作者 郭柏辛
Kuo, Pohsin
貢獻者 宋傳欽
郭柏辛
Kuo, Pohsin
關鍵詞 條件機率分配
相容性
比值矩陣
特徵向量法
奇異值分解法
最近似秩1矩陣法
類Frobenius範數
Lagrange乘數法
高維度牛頓法
最佳化法
最近似聯合分配
conditional probability distribution
compatibility
ratio matrix
eigenvector approach
singular value decomposition approach
most nearly rank one matrix approach
semi-Frobenius norm
Lagrange multiplier method
multivariate Newton`s method
optimization method
most nearly joint distribution
日期 2016
上傳時間 11-Jul-2016 17:42:15 (UTC+8)
摘要 給定二元隨機向量(X,Y)之聯合機率分配,可容易得到其條件機率分配X|Y與Y|X;反之,給定條件機率分配X|Y與Y|X,是否能獲得對應的聯合機率分配呢?條件分配相容性研究的主要內容包括:(一)如何判斷給定的條件分配是否相容?(二)若相容,則如何找到聯合分配?(三)若不相容,則該如何找到最近似的聯合分配?

根據比值矩陣法的理論,檢驗比值矩陣是否為秩1矩陣或者有擴張秩1矩陣,便可得知給定的條件分配是否相容。當比值矩陣的元素皆為正值時,本文運用線性代數中之奇異值分解定理,先發展出奇異值分解法來處理條件分配相容性問題;當比值矩陣的元素非皆為正值時,接續發展出最近似秩1矩陣法來解決相容性問題,而最近似秩1矩陣法可視為奇異值分解法的延伸。在發展最近似秩1矩陣法時,我們利用到類Frobenius範數的概念,並提出了三種求解過程(無限制條件法、Lagrange乘數法與高維度牛頓法)以及相關的演算法。本文詳細剖析了三種求解過程之數學流程,並輔以實際例子予以說明。

當條件機率分配不相容時,我們通常可獲得兩組近似聯合分配。如何將它們做適當的組合,也是值得探討的問題。最後,針對等加權之組合方式、權重與總誤差成反比之組合方式以及特徵向量法之組合方式進行比較分析。
Given a bivariate joint distribution of random vector (X,Y), we can easily derive the conditional probability distributions of X|Y and Y|X. Conversely, given conditional probability distributions of X|Y and Y|X, can we find the corresponding joint distribution? The compatibility issues of conditional distribution include: (a) how to determine whether they are compatible; (b) how to find the joint distribution if they are compatible; (c) how to find the most nearly joint distribution if they are incompatible.

Using the theory of ratio matrix approach, we can determine the given conditional probability distributions are compatible or not by checking whether their corresponding ratio matrix or the extension matrix of this ratio matrix is rank one or not. When elements of the ratio matrix are all positive, this thesis uses the singular value decomposition theorem of linear algebra to develop the singular value decomposition approach to deal with the compatibility issues. When elements of the ratio matrix are not all positive, we provide the most nearly rank one matrix approach to solve the compatibility issues. This most nearly rank one matrix approach can be considered as the extension of singular value decomposition approach. To develop the most nearly rank one matrix approach, we use the concept of semi-Frobenius norm to provide three solving methods (unconstrained method, Lagrange multiplier method, and multivariate Newton`s method) with related algorithms. This thesis gives the mathematical procedure on these three solving methods in detail and uses examples to explain the compatibility issues.

When the conditional distributions are incompatible, we usually have two nearly joint distributions. It would be worth of discussing the combination of these two nearly joint distributions. Hence, this thesis compares and analyzes the compatibility issues with three different weights, which are equal, inverse proportional to the total errors, and relating to eigenvectors.
參考文獻 Arnold, B. C. and Press, S. J. (1989), Compatible conditional distributions. Journal of the American Statistical Association, 84, 152-156.
Arnold, B. C., Castillo, E., and Sarabia, J. M. (2002), Exact and near compatibility of discrete conditional distributions. Computational Statistics \\& Data Analysis, 40, 231-252.
Arnold, B. C., Castillo, E., and Sarabia, J. M. (2004), Compatibility of partial or complete conditional probability specifications. Journal of Statistical Planning and Inference, 123, 133-159.
David Poole (2006), Linear algebra: a modern introduction. Brooks/Cole, Cengage Learning.
Markovsky, Ivan (2011), Low rank approximation: algorithms, implementation, applications. Springer Science \\& Business Media.
Song, C. C., Li, L. A., Chen, C. H., Jiang, T. J., and Kuo, K. L. (2010), Compatibility of finite discrete conditional distributions. Statistical Sinica, 20, 423-440.
周志成(2016),奇異值分解,檢索日期:2016年6月3日,檢自:https://ccjou.wordpress.com/2009/09/01/
周志成(2016),牛頓法,檢索日期:2016年6月3日,檢自:https://ccjou.wordpress.com/2013/07/08/
顧仲航(2011),以特徵向量法解條件分配相容性問題,國立政治大學應用數學系碩士論文。
描述 碩士
國立政治大學
應用數學系
102751014
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102751014
資料類型 thesis
dc.contributor.advisor 宋傳欽zh_TW
dc.contributor.author (Authors) 郭柏辛zh_TW
dc.contributor.author (Authors) Kuo, Pohsinen_US
dc.creator (作者) 郭柏辛zh_TW
dc.creator (作者) Kuo, Pohsinen_US
dc.date (日期) 2016en_US
dc.date.accessioned 11-Jul-2016 17:42:15 (UTC+8)-
dc.date.available 11-Jul-2016 17:42:15 (UTC+8)-
dc.date.issued (上傳時間) 11-Jul-2016 17:42:15 (UTC+8)-
dc.identifier (Other Identifiers) G0102751014en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/98903-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用數學系zh_TW
dc.description (描述) 102751014zh_TW
dc.description.abstract (摘要) 給定二元隨機向量(X,Y)之聯合機率分配,可容易得到其條件機率分配X|Y與Y|X;反之,給定條件機率分配X|Y與Y|X,是否能獲得對應的聯合機率分配呢?條件分配相容性研究的主要內容包括:(一)如何判斷給定的條件分配是否相容?(二)若相容,則如何找到聯合分配?(三)若不相容,則該如何找到最近似的聯合分配?

根據比值矩陣法的理論,檢驗比值矩陣是否為秩1矩陣或者有擴張秩1矩陣,便可得知給定的條件分配是否相容。當比值矩陣的元素皆為正值時,本文運用線性代數中之奇異值分解定理,先發展出奇異值分解法來處理條件分配相容性問題;當比值矩陣的元素非皆為正值時,接續發展出最近似秩1矩陣法來解決相容性問題,而最近似秩1矩陣法可視為奇異值分解法的延伸。在發展最近似秩1矩陣法時,我們利用到類Frobenius範數的概念,並提出了三種求解過程(無限制條件法、Lagrange乘數法與高維度牛頓法)以及相關的演算法。本文詳細剖析了三種求解過程之數學流程,並輔以實際例子予以說明。

當條件機率分配不相容時,我們通常可獲得兩組近似聯合分配。如何將它們做適當的組合,也是值得探討的問題。最後,針對等加權之組合方式、權重與總誤差成反比之組合方式以及特徵向量法之組合方式進行比較分析。
zh_TW
dc.description.abstract (摘要) Given a bivariate joint distribution of random vector (X,Y), we can easily derive the conditional probability distributions of X|Y and Y|X. Conversely, given conditional probability distributions of X|Y and Y|X, can we find the corresponding joint distribution? The compatibility issues of conditional distribution include: (a) how to determine whether they are compatible; (b) how to find the joint distribution if they are compatible; (c) how to find the most nearly joint distribution if they are incompatible.

Using the theory of ratio matrix approach, we can determine the given conditional probability distributions are compatible or not by checking whether their corresponding ratio matrix or the extension matrix of this ratio matrix is rank one or not. When elements of the ratio matrix are all positive, this thesis uses the singular value decomposition theorem of linear algebra to develop the singular value decomposition approach to deal with the compatibility issues. When elements of the ratio matrix are not all positive, we provide the most nearly rank one matrix approach to solve the compatibility issues. This most nearly rank one matrix approach can be considered as the extension of singular value decomposition approach. To develop the most nearly rank one matrix approach, we use the concept of semi-Frobenius norm to provide three solving methods (unconstrained method, Lagrange multiplier method, and multivariate Newton`s method) with related algorithms. This thesis gives the mathematical procedure on these three solving methods in detail and uses examples to explain the compatibility issues.

When the conditional distributions are incompatible, we usually have two nearly joint distributions. It would be worth of discussing the combination of these two nearly joint distributions. Hence, this thesis compares and analyzes the compatibility issues with three different weights, which are equal, inverse proportional to the total errors, and relating to eigenvectors.
en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究架構 3
第二章 文獻探討 4
第一節 比值矩陣法 4
第二節 數學規劃法 9
第三節 特徵向量法 12
第三章 奇異值分解法 15
第一節 奇異值分解定理 15
第二節 奇異值分解法 23
第三節 實例分析 26
第四章 最近似秩1矩陣法 32
第一節 最近似秩1矩陣 32
第二節 無限制條件法 34
第三節 Lagrange乘數法 37
第四節 高維度牛頓法 42
第五節 實例探討 47
第五章 最近似聯合分配 55
第一節 最近似聯合分配 55
第二節 各種組合方式的比較 58
第六章 結論 65
zh_TW
dc.format.extent 1236917 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102751014en_US
dc.subject (關鍵詞) 條件機率分配zh_TW
dc.subject (關鍵詞) 相容性zh_TW
dc.subject (關鍵詞) 比值矩陣zh_TW
dc.subject (關鍵詞) 特徵向量法zh_TW
dc.subject (關鍵詞) 奇異值分解法zh_TW
dc.subject (關鍵詞) 最近似秩1矩陣法zh_TW
dc.subject (關鍵詞) 類Frobenius範數zh_TW
dc.subject (關鍵詞) Lagrange乘數法zh_TW
dc.subject (關鍵詞) 高維度牛頓法zh_TW
dc.subject (關鍵詞) 最佳化法zh_TW
dc.subject (關鍵詞) 最近似聯合分配zh_TW
dc.subject (關鍵詞) conditional probability distributionen_US
dc.subject (關鍵詞) compatibilityen_US
dc.subject (關鍵詞) ratio matrixen_US
dc.subject (關鍵詞) eigenvector approachen_US
dc.subject (關鍵詞) singular value decomposition approachen_US
dc.subject (關鍵詞) most nearly rank one matrix approachen_US
dc.subject (關鍵詞) semi-Frobenius normen_US
dc.subject (關鍵詞) Lagrange multiplier methoden_US
dc.subject (關鍵詞) multivariate Newton`s methoden_US
dc.subject (關鍵詞) optimization methoden_US
dc.subject (關鍵詞) most nearly joint distributionen_US
dc.title (題名) 二維條件分配相容性問題之新解法zh_TW
dc.title (題名) A new approach to solve the compatibility issues for two-dimensional conditional distributionsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Arnold, B. C. and Press, S. J. (1989), Compatible conditional distributions. Journal of the American Statistical Association, 84, 152-156.
Arnold, B. C., Castillo, E., and Sarabia, J. M. (2002), Exact and near compatibility of discrete conditional distributions. Computational Statistics \\& Data Analysis, 40, 231-252.
Arnold, B. C., Castillo, E., and Sarabia, J. M. (2004), Compatibility of partial or complete conditional probability specifications. Journal of Statistical Planning and Inference, 123, 133-159.
David Poole (2006), Linear algebra: a modern introduction. Brooks/Cole, Cengage Learning.
Markovsky, Ivan (2011), Low rank approximation: algorithms, implementation, applications. Springer Science \\& Business Media.
Song, C. C., Li, L. A., Chen, C. H., Jiang, T. J., and Kuo, K. L. (2010), Compatibility of finite discrete conditional distributions. Statistical Sinica, 20, 423-440.
周志成(2016),奇異值分解,檢索日期:2016年6月3日,檢自:https://ccjou.wordpress.com/2009/09/01/
周志成(2016),牛頓法,檢索日期:2016年6月3日,檢自:https://ccjou.wordpress.com/2013/07/08/
顧仲航(2011),以特徵向量法解條件分配相容性問題,國立政治大學應用數學系碩士論文。
zh_TW