105年第1學期-6189 統計計算 課程資訊

評分方式

評分項目	配分比例	說明
Homework assignments	30
Midterm and/or Presentations	30
Final and/or Projects	40

教育目標

Introducing some computational techniques and algorithms used by statistical researchers and practitioners 1. Monte Carlo Methods (Integration and Optimization) 2. Markov Chain Monte Carlo 3. Resampling Methods (Bootstrap, Jackknife, and Cross-Validaton) 4. Data Mining (Trees, Neural Networks, and Support Vector Machines)

基本資料

選修課，學分數：3-0
上課時間：三/5,6,7[M442]
修課班級：統計碩博1,2
修課年級：年級以上
選課備註：

參考書目

a. Monte Carlo Statistical Methods by Christian P. Robert & George Casella
b. The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman
c. Statistical Computing by William J. Kennedy & James E. Gentle
d. Elements of Statistical Computing by Ronald A. Thisted
e. Bayesian Statistical Modeling by Peter Congdon
f. An Introduction to the Bootstrap by Bradley Efron and Robert J. Tibshirani
g. Simulation by Sheldon M. Ross
h. An Introduction to Statistical Learning With Application in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani