课程号：00137960
课程名称：统计思维
开课学期：春
学分： 3
先修课程：微积分、线性代数
基本目的：该课程提供一个较为系统、基础、前沿性的统计学概论，课程主要包含：统计学原理、统计推理、贝叶斯推理、统计模型和方法等。帮助学生学会如何实现或应用统计学方法和模型，并能掌握统计学所蕴含的数学机理，从而培养学生的统计分析与思维能力。
内容提要：
1 Basic Concepts and Applications
1）Likelihood 2）Sufficiency 3）Exponential family 4）Frequentist 5）Minimax theory
2 Interpretations of Uncertainty
3 Statistical Inference
1）Inference, learning and information 2）parametric and nonparametric methods
3）The Bootstrap 4）Hypothesis testing and p-values
4 Bayesian Inference
1）The Bayesian paradigm 2）Parametric models 3）Statistical decision theory
5 Statistical Models and Methods
1）Linear models 2）Generalized linear models 3）Generalized additive models
4）Random effect modes 5）Robust estimation 6）Online learning
6 Nonparametric Statistics
1）The bootstrap and the jackknife 2）Nonparametric regression 3）Density estimation
7 Advantaged Topics
1）Design and analysis of experiments 2）A/B test 3）Empirical Bayes
4）False discovery rate 5）Large-scale hypothesis testing
教学方式：课堂讲授，每周3学时
教材与参考书：
1. Cox, D. R.(2006).Principles of Statistical Inference.Cambridge University Press.
2. Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press.
3. Rao, C. R. (1997). Statistics and Truth: Putting Chance to Work (2nd ed.). World Scientific.
4. Stigler, S. M. (2016). The Seven Pillars of Statistical Wisdom. Harvard University Press.
学生成绩评定方法：平时作业50%，期末论文50%。
课程修订负责人：张志华 林伟