一、課程說明(Course Description)
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<br>課程內容涵蓋常用於解決統計推論問題之演算法
<br>以及這些方法之理論基礎
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<br> 二、指定用書(Text Books)
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<br>無
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<br> 三、參考書籍(References)
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<br>1. Elements of Computational Statistics (2002), Gentle, Springer.
<br>2. Computational Statistics (2005), Givens and Hoeting, Wiley.
<br>3. Bayesian Data Analysis (1995), Gelman, Carlin, Stern and Rubin, 
<br>   Chapman & Hall.  
<br>4. An Introduction to the Bootstrap (1993), Efron and Tibshirani, 
<br>   Chapman & Hall. 
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<br> 四、教學方式(Teaching Method)
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<br>口述
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<br> 五、教學進度(Syllabus)
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<br>教學內容將涵蓋以下主題
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<br> Introduction to R
<br> Random number generation
<br> Monte Carlo methods
<br> Numerical approximation for an expectation
<br> Optimization
<br> EM algorithm and its generalizations
<br> Bootstrap method
<br> Bayesian analysis and Markov chain Monte Carlo
<br> Other topics: smoothing , cross validation…
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<br> 六、成績考核(Evaluation)
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<br> 作業         40% 
<br> 期中考       30% 
<br> 期末報告     30%
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<br> 七、可連結之網頁位址
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<br>放置於學校e-learning 教學平台下
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