一、教學目標：

Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems. This course covers the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach.

二、教材內容：

   1. Introduction of R
   2. Methods for Generating Random Variables  
      Transformation and Inverse method
      Box-Muller and Polar Marsaglia method
      Bivariate Normal Random Variables
      Accept-Reject Methods
   4.Monte Carlo Integration and variance reduction
      Buffon`s Needle problem
      Antithetic variate, control variate
      Stratified sampling
      Importance Sampling
      Stratified Importance Sampling
   5. Monte Carlo Methods for Inferential Statistics
      Classical Inferential Statistics
      Monte Carlo Methods for Inferential Statistics
      Bootstrap Methods  Moore14.pdf
  6. Data Partitioning
      Cross-Validation
      Jackknife
      Jackknife-after-Bootstrap
  7. Permutation Tests
      Tests for Equal Distributions
      Multivariate Tests for Equal Distributions
  8.Markov Chain Monte Carlo
      The Metropolis-Hastings algorithm
      Gibbs sampler.
  9.Kernel Density Estimation
     Univariate Density Estimation
     Kernel Density Estimation
     Bivariate and Multivariate Density Estimation


三、實施方式：
    作業與上機演練

四、參考書目：
    Rizzo, M. L., Statistical computing with R
    Morgan, B.J.T., Elements of Simulation
    Robert, C. P., Casella, G. Monte Carlo Statistical Methods
    Martine W. L. Computational Statistics Handbook with MATLAB Second Edition