1 Introduction 1.1 GUI and Basic functions 1.1.1 Command Window 1.1.2 Command History 1.1.3 MatLab Help 2 Data in MatLab 2.1 Manipulating data 2.1.1 Creating Objects 2.1.2 Operators 3 Graphics 3.1 Use plotting tools 3.2 Use the command interface 3.2.1 Basic plots 3.2.2 Adding Plots to an Existing Graph 3.2.3 Multiple Plots in One Figure 3.2.4 Controlling the Axes 3.2.5 Axis Labels and Titles 3.3 Mesh and Surface Plots 3.4 Creating Specialized Plots 3.5 Advanced plotting

1 Introduction 2 SAS Language 2.1 Proc Step and Data Step 2.2 SAS Logical Library 2.2.1 Access SAS file 2.2.2 View SAS library and file 3 SAS Programming 3.1 Reading data by DATA STEP 3.2 Output format 3.3 Manipulate datasets 3.3.1 SET statement 3.3.2 SORT proc 3.4 Logical statements 3.4.1 IF-THEN statement 3.4.2 SELECT-WHEN statement 3.4.3 DO-ENDS statement 3.4.4 DO-WHILE DO-UNTIL statement 3.5 OPERATIONS 4 Basic statistical analysis 4.1 Descriptive Statistics Proc 4.1.1 MEANS proc 4.1.2 SUMMARY proc 4.1.3 UNIVARIATE proc 4.1.4 TABULATE PROC 4.1.5 GCHART proc 4.1.6 GPLOT proc 4.2 INFERENTIAL Statistics 4.2.1 T-TEST 4.2.2 Chi-square tests 4.2.3 Correlation 4.2.4 Regression

1 Programming 1.1 M file 1.1.1 Program Control Statements 1.1.2 M-File Functions 1.2 anonymous functions 2 Computational statistics with Matlab 2.1 Functions on Probability and Statistics 2.1.1 Probability distribution 2.1.2 Descriptive statistics 2.1.3 Statistical plotting 2.1.4 Linear model 2.1.5 Multivariate Statistics 2.2 Monte Carlo with Matlab 2.2.1 Monte Carlo Assessment of Hypothesis Testing 2.2.2 MCMC with matlab 3 Symbolic computation with matlab 3.1 Creating Symbolic Variables and Expressions 3.2 Calculus 4 Optimization 4.1 Unconstrained Minimization Example 4.2 Nonlinear Inequality Constrained Example

1 Numerical optimization methods in R 1.1 Root-finding in one dimension 1.1.1 Bisection method 1.1.2 Brent’s method 1.1.3 Newton’s method 1.1.4 Fisher scoring 1.2 multivariate optimization 1.2.1 Newton’s method and Fisher scoring 1.3 Numerical Integration 1.4 Maximum Likelihood Problems 1.5 Optimization Problems 1.5.1 One-dimension Optimization 1.5.2 multi-dimensional Optimization 1.6 Linear Programming

1 Markov Chain Monte Carlo Methods 1.4 The Gibbs Sampler 1.4.1 The Slice Gibbs Sampler 1.5 Monitoring Convergence 1.5.1 Convergence diagnostics plots 1.5.2 Monte Carlo Error 1.5.3 The Gelman-Rubin Method 1.6 WinBUGS Introduction 1.6.1 Building Bayesian models in WinBUGS 1.6.2 Model specification in WinBUGS 1.6.3 Data and initial value specification 1.6.4 Compiling model and simulating values

1 Bootstrap and Jackknife 1.1 The Bootstrap 1.1.1 Bootstrap Estimation of Standard Error 1.1.2 Bootstrap Estimation of Bias 1.2 Jackknife 1.3 Jackknife-after-Bootstrap 1.4 Bootstrap Confidence Intervals 1.4.1 The Standard Normal Bootstrap Confidence Interval 1.4.2 The Percentile Bootstrap Confidence Interval 1.4.3 The Basic Bootstrap Confidence Interval 1.4.4 The Bootstrap t interval 1.5 Better Bootstrap Confidence Intervals 1.6 Application: Cross Validation

1 Monte Carlo Integration and Variance Reduction 1.1 Monte Carlo Integration 1.1.1 Simple Monte Carlo estimator 1.1.2 Variance and Efficiency 1.2 Variance Reduction 1.3 Antithetic Variables 1.4 Control Variates 1.4.1 Antithetic variate as control variate 1.4.2 Several control variates 1.5 Importance sampling 1.6 Stratified Sampling 1.7 Stratified Importance Sampling

1 Graphics with R 1.1 Managing graphics 1.1.1 Graphical Functions 1.1.2 Low-level plotting commands 1.1.3 Graphical Parameters 2 Statistical Analysis with R 2.1 Formulae 2.2 Generic Functions 2.3 Packages 3 Programming with R 3.1 Flow Control 3.2 Functions 3.3 Miscellaneous programming tips 3.4 Debugging 3.5 Efficient programming 3.6 R script editors

1 TEX介绍 2 TEX的宏包和扩展 3 环境集 4 LATEX命令集 5 页面版式命令 6 计数器命令 7 目录表 8 交叉引用和索引 9 宏包 10 LATEX中文化

1 Methods for Generating Random Variables 1.1 Generating Uniform(0,1) random number 1.2 Random Generators of Common Probability Distribution in R 1.2.1 The Inverse Transform Method 1.2.2 The Acceptance-Rejection Method 1.2.3 Transformation Methods 1.2.4 Sums and Mixtures 1.3 Multivariate Distribution 1.3.1 Multivariate Normal Distribution 1.3.2 Mixtures of Multivariate Normals 1.3.3 Wishart Distribution 1.3.4 Uniform Distribution on the d−Sphere 1.4 Stochastic Process