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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Dirichlet分配,非短視行為,與演化性賽局 / Dirichlet Distribution, Non-myopic Behavior, and Evolutionary Game

洪明君 Unknown Date (has links)
No description available.
2

對平滑直方圖的貝氏與準貝氏方法之比較 / A comparison on Bayesian and quasi-Bayesian methods for Histogram Smoothing

彭志弘, Peng, Chih-Hung Unknown Date (has links)
針對具有多項分配(multinomial distribution)母體的類別資料,貝氏分析通常採取Dirichlet分配作為其先驗分配(prior distribution),但在很多實際應用時,卻會遭遇困難;例如,當我們欲推估各年齡層佔總勞動力人口之比例時,母體除具多項分配外,其相鄰類別之比例亦相對接近;換言之,此時母體為具有平滑性(smooth)的多項分配,若依然採用Dirichlet分配作為其先驗分配,則會因為Dirichlet分配本身不具有平滑的特性,因而在做貝氏分析時會產生困擾。對這個難題Dickey and Jiang於1998年提出一個解決之道,他們的理論是對Dirichlet分配作適當之調整,將經過線性轉換後之Dirichlet分配稱為過濾後Dirichlet分配(filtered-variate Dirichlet distribution),以過濾後Dirichlet分配作為調整後之先驗分配。對於Dickey and Jiang提出的方法,我們重新以蒙地卡羅法(Monte Carlo method)求出貝氏解,同時也嘗試以類似Makov and Smith (1977)和Smith and Makov (1978)對混合分配(mixture distribution)所用之準貝氏方法(quasi-Bayesian method)來逼近貝氏解。而本文將由電腦模擬的方式,探討貝氏方法與準貝氏方法之執行結果,並且考察準貝氏方法之收斂行為,對準貝氏方法的使用時機提出建議。
3

多變量d轉換的一些應用 / Some applications of multivariate d-transformations

郭錕霖 Unknown Date (has links)
Jiang (1997) 首先提出多變量d轉換與其性質。利用多變量d轉換,我們可以定義新式的特徵函數,並且稱它們是多變量d特徵函數。在這篇論文中,我們將使用多變量d特徵函數來證明在普通的條件下,Dirichlet隨機向量的線性組合會分配收斂(converge in distribution)到一個對稱的分配。此外,當給定一個分配函數的多變量d特徵函數,我們將建構一個方法來決定此分配函數。另一方面,我們將證明多變量d特徵函數擁有很多類似傳統的特徵函數的性質。 / A multivariate d-transformation and its properties were first given by Jiang (1997). By means of the multivariate d-transformations, we can define new kinds of characteristic functions and call them multivariate d-characteristic functions. In this thesis, we will use the multivariate d-characteristic function to show that the linear combinations of Dirichlet random vectors, under regularity conditions, converge in distribution to a spherical distribution. Moreover, We will construct a method for constructing the distribution function with a given multivariate d-characteristic function. In addition, we will show that the multivariate d-characteristic function has many properties which are similar to those of the traditional characteristic function.

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