Title

潛在類別模型的蒙地卡羅馬可夫鍊分析

Translated Titles

Latent Class Models in Monte Carlo Markov Chain Analysis

DOI

10.6840/cycu201300956

Authors

尹維烘

Key Words

蒙地卡羅馬可夫鍊 ; EM 演算法 ; 最大概似估計 ; 潛類別模型 ; Monte Carlo Markov Chain ; EM algorithm ; Maximum Likelihood Estimation ; Latent Class Model

PublicationName

中原大學應用數學研究所學位論文

Volume or Term/Year and Month of Publication

2013年

Academic Degree Category

碩士

Advisor

林余昭;鄭子韋

Content Language

繁體中文

Chinese Abstract

潛類別模型概念簡單、容易了解。潛在類別參數的傳統估計方法有最大概似估計、EM 演算法。本文將使用WinBUGS軟體,利用蒙地卡羅馬可夫鍊的方法去估計潛在類別參數及條件機率,並將其應用於中原大學101年下學期微積分第一次會考成績的分析。

English Abstract

The concepts of latent class models are easy to understand.Maximum likelihood estimations (MLE) and EM algorithm are two methods used to estimate the latent class parameters. In this paper, we use the WinBUGS software and the Monte Carlo Markov Chain (MCMC) methods to estimate these latent class parameters and conditional probabilities. We apply the techniques to analyze the CYCU calculus examinations.

Topic Category 基礎與應用科學 > 數學
理學院 > 應用數學研究所
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