一、課程說明(Course Desc<x>ription)
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<br>本課程涵蓋統計推論常用的數值演算法，及這些方法之理論基礎。
<br>(修課同學需具備基本程式能力，作業與專題報告都需要程式編碼) 
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<br> 二、指定用書(Text Books)
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<br>The elements of Statistical Learning (2009), 
<br>Hastie, Tibshirani and Friedman, Springer.
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<br> 三、參考書籍(References)
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<br>1. Bayesian Data Analysis (1995), Gelman, Carlin, Stern and Rubin, 
<br>   Chapman & Hall.  
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<br>2. Convex Optimization (2004), Stephen Boyd and Lieven Vandenberghe,
<br>   Cambridge University Press.
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<br>3. Computational Statistics (2005), Givens and Hoeting, Wiley.
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<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         50% 
<br> 專題報告     50%
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<br> 七、可連結之網頁位址
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