贝叶斯统计

学分：3学分

先修课程：概率论、数理统计

课程简介：

贝叶斯学派是数理统计中一个重要的学派，它有鲜明的特点和独到的处理方法，在国际上贝叶斯学派与非贝叶斯学派的争论是很多的。本课程重点介绍贝叶斯统计推断的理论、方法及其基本观点，同时对贝叶斯方法和经典方法在历史上的重大分歧也适当地予以介绍。通过本课程的学习能系统地掌握贝叶斯统计的基本理论、方法和应用。主要内容有：先验分布与后验分布的基本概念、后验分布的计算方法、估计及假设检验、贝叶斯统计决策方法等。

课程目标：

了解贝叶斯统计的基本思想，掌握贝叶斯统计的基本方法，能够运用贝叶斯统计去认识和解决一些实际问题。

考核方式：

平时成绩（考勤、作业、分组讨论等）30%，期末考试成绩70%

课程教材：

Peter M Lee, Bayesian Statistics, 4th edition, ISBN: 1118332571, ISBN13: 9781118332573.

Bayesian Statistics

Credits: 3 Credits

Pre-requisite: Probability, Mathematical Statistics

Module Description：

Bayesian Statistics is an important branch of mathematical statistics; it has distinctive characteristics and unique approach. There are many debates about school of Bayesian and non-Bayesian in the world. This course focuses on Bayesian statistical inference theory, methods and basic ideas, while the major differences of Bayesian methods and classical methods in the history is also appropriate to be introduced. Through this course, we can systematically master the basic theory, methods and application of Bayesian statistics. The main contents are: the basic concepts of priori distribution and the posterior distribution, calculation methods of the posterior distribution, estimate and hypothesis testing, Bayesian statistical decision-making methods, and so on.

Learning Outcome and Objectives：

By the end of the module the student should be able to: understand the basic idea of Bayesian statistics; master the basic method of Bayesian statistics;apply Bayesian statistics to understand and solve some practical problems.

Assessment：

Assignments and Class Performance 30%

Final Exam 70%

Textbook and Reference：

Peter M Lee, Bayesian Statistics, 4th edition, ISBN: 1118332571, ISBN13: 9781118332573.