本課程是博士班的必修課程，主要是對統計推論的理論與方法作一紮實及完整的介紹，以為將來修習其他統計相關課程奠定基礎。

This course is a required course for doctoral students. It mainly provides a solid and complete introduction to the theory and methods of statistical inference, laying a foundation for studying other statistics-related courses in the future.

統計推論是博士班的必修課程，也是學生學習統計理論的核心課程，為其他統計相關課程的理論基礎，課程主要培養學生紮實的統計理論與具備從事研究的能力。課程內容包括機率理論和統計推論兩部分，其中機率部分介紹基本定義、定理、分配與各種收斂有關的大樣本理論，而統計部分介紹點估計、區間估計與假設檢定等各種相關的理論與方法。

Statistical inference is a required course for doctoral classes and a core course for students to learn statistical theory. It serves as the theoretical basis for other statistics-related courses. The course mainly cultivates students' solid statistical theory and the ability to engage in research. The course content includes two parts: probability theory and statistical inference. The probability part introduces basic definitions, theorems, distribution and various convergence-related large sample theories, while the statistics part introduces various related theories and methods such as point estimation, interval estimation and hypothesis testing.

G. Casella and R.L. Berger: Statistical Inference (second edition)
E.L. Lehmann and G. Casella: Theory of Point Estimation (second edition)

G. Casella and R.L. Berger: Statistical Inference (second edition)
E.L. Lehmann and G. Casella: Theory of Point Estimation (second edition)