本課程設計是針對非主修統計或數學的碩士畢業學生所開設的先修課程，主要是對機率論作一紮實及完整的介紹，以為將來修習統計推論課程奠定基礎。

This course is designed as a prerequisite course for master's degree students who are not majoring in statistics or mathematics. It mainly provides a solid and complete introduction to probability theory to lay the foundation for future statistical inference courses.

本課程設計是為未曾修習碩士層級之機率論的同學所開設的先修課程，主要是對機率論作一紮實及平衡的介紹，以為將來修習統計推論課程奠定基礎。
修習本課程只需具備集合論及高等微積分的數學訓練。課程內容包含：機率公設事件及事件之機率、運算法則、隨機變數及它們的分配、動差母函數及動差、隨機向量、一些特殊的分配、極限理論等。

This course is designed as a preparatory course for students who have never studied probability theory at the master's level. It is mainly intended to provide a solid and balanced introduction to probability theory to lay the foundation for future statistical inference courses.
Taking this course only requires mathematical training in set theory and advanced calculus. Course content includes: postulates of probability events and the probabilities of events, algorithms, random variables and their distribution, dynamic difference generating functions and dynamic differences, random vectors, some special distributions, limit theory, etc.

1. Hogg, R.V., McKean, J.W. and Craig, A.T.: Introduction to Mathematical Statistic (seventh edition)
2. Chung, K.L.: A Course in Probability Theory
3. Billingsley, P.: Probability and Measure

1. Hogg, R.V., McKean, J.W. and Craig, A.T.: Introduction to Mathematical Statistic (seventh edition)
2. Chung, K.L.: A Course in Probability Theory
3. Billingsley, P.: Probability and Measure