1 Introduction 1.1 R website 1.2 Differences between R and S 1.3 Start with R 2 Data with R 2.1 Objects 2.2 Reading data in a file 2.3 Saving data 2.4 generating data 2.5 Manipulating objects 2.5.1 Creating objects 2.5.2 Operators 2.5.3 Accessing the values of an object: the indexing system 2.5.4 Accessing the values of an object with names 2.5.5 Arithmetics and simple functions 2.5.6 Matrix Computation

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

短路的一般概念 恒定电势源电路的三相短路 同步发电机的基本方程 同步电机的三相短路 电力系统三相短路的实用计算

短路的一般概念 恒定电势源电路的三相短路 同步发电机的基本方程 同步电机的三相短路 电力系统三相短路的实用计算

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 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 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 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 EM optimization method 1.1 EM algorithm 1.2 Convergence 1.3 Usage in exponential families 1.4 Usage in finite normal mixtures 1.5 Variance estimation 1.5.1 Louis method 1.5.2 SEM algorithm 1.5.3 Bootstrap method 1.5.4 Empirical Information 1.6 EM Variants 1.6.1 Improving the E step 1.6.2 Improving the M step 1.7 Pros and Cons

1 Markov Chain Monte Carlo Methods 1.1 Introduction 1.1.1 Integration problems in Bayesian inference 1.1.2 Markov Chain Monte Carlo Integration 1.1.3 Markov Chain 1.2 The Metropolis-Hastings Algorithm 1.2.1 Metropolis-Hastings Sampler 1.2.2 The Metropolis Sampler 1.2.3 Random Walk Metropolis 1.2.4 The Independence Sampler 1.3 Single-component Metropolis Hastings Algorithms 1.4 Application: Logistic regression