一、課程說明(Course Description)
課程內容涵蓋常用於解決統計推論問題之演算法
以及這些方法之理論基礎
二、指定用書(Text Books)
無
三、參考書籍(References)
1. Elements of Computational Statistics (2002), Gentle, Springer.
2. Computational Statistics (2005), Givens and Hoeting, Wiley.
3. Bayesian Data Analysis (1995), Gelman, Carlin, Stern and Rubin,
Chapman & Hall.
4. An Introduction to the Bootstrap (1993), Efron and Tibshirani,
Chapman & Hall.
四、教學方式(Teaching Method)
口述
五、教學進度(Syllabus)
教學內容將涵蓋以下主題
Introduction to R
Random number generation
Monte Carlo methods
Numerical approximation for an expectation
Optimization
EM algorithm and its generalizations
Bootstrap method
Bayesian analysis and Markov chain Monte Carlo
Other topics: smoothing , cross validation…
六、成績考核(Evaluation)
作業 50%
期中考 25%
期末報告 25%
七、可連結之網頁位址