Department of Statistics
Bayesian Analysis
(貝氏分析)
Spring 2013 (101 學年度第 2 學期)
1. This mission of the College is to serve business and society in the global economy through
developing quality and socially responsible professionals and business leaders.
2. The strategic objective of Department of Statistics is to cultivate quality professionals with
enthusiasm and global perspectives.
Graduate Program Learning Goals (goals covered by this course are indicated):

1
Graduate students should be able to communicate effectively verbally and in writing.

2

4
Graduate students should solve strategic problems with a creative and innovative approach.
Graduate students should demonstrate leadership skills and ethic demanded of a person in
authority.
Graduate students should possess a global economic and management perspective.

5
Graduate students should possess the necessary skills and values demanded of a true professional.
3
 Instructor/開課教師:
Kuo-Jung Lee /李國榮
kjlee@stat.ncku.edu.tw
(06)2757575#53627
 Prerequisite/先修科目:
Prior exposure to statistics is a requirement for this class. Any additional exposure to
statistical inference, statistical computing, calculus and matrix algebra will be useful for the
class.
 Course Description/課程描述:
The course covers fundamental probability theory, Bayesian statistics, and Bayesian
applications to business/social/medical data. After reviewing probability theory, we intend
to cover Bayesian analyses and their applications on business/social/medical science. Each
student will expect to running analyses every class period. We also plan to distribute
supplementary readings that offer both theoretical and practical discussions of Bayesian
statistics in business/social/medical science.
 Course Objectives/課程目標:
This is a course in Bayesian statistics. Bayesian inference is a powerful and increasingly popular
statistical approach, which allows one to deal with complex problems in a conceptually simple and
unified way. The recent introduction of Markov Chain Monte Carlo (MCMC) simulation methods
has made possible the solution of large problems in Bayesian inference that were formerly
intractable. This course will introduce the student to the basic methods and techniques of modern
Bayesian inference, including parameter estimation, MCMC simulation, hypothesis testing, and
model selection/model averaging in the context of practical problems. The main topics emphasized
in this course are:
1. on how to choose priors distributions and how to calculate posterior distributions;
2. on how to use Bayesian thinking to develop models for data with complex structure.
 Course Content/課程內容:
This is a tentative schedule.
Date
Chapter
Topic
February 1
An Introduction to R.
February 2
Introduction to Bayesian Thinking.
February 3
Single-Parameter Models.
March
4
Multiparameter Models.
March
5
Introduction to Bayesian Computation.
March
6
Markov Chain Monte Carlo Methods.
April
7
Hierarchical Modeling.
April
8
Model Comparison.
April
9
Regression Models.
May
10
Gibbs Sampling.
May
11
Using R to Interface with WinBUGS.
June
11
Final project presentation.
June
11
Final project presentation.
 Textbook/教科書:
Book: Bayesian Computation with R.
Authors: Jim Albert.
Pub.: Springer Verlag. ISBN: 978-0-387-92297-3
 References/參考書目:
1. Bayesian Methods: A Social and Behavioral Sciences Approach 2nd Ed. Jeff Gill
2. Bayesian Data Analysis 2nd Ed. Andrew Gelman et. al.
3. Monte Carlo Statistical Methods 2nd Ed. C. R. Robert and G. Casella.
 Grading Policy/評量方式:
Your final grade for this course will be determined almost entirely by your performance on
homeworks, midterms, and the final project. I expect the distribution of points (and thus the
corresponding percentages) to be as follows:
Homework 30 %
Midterm I 20 %
Midterm II 20 %
Final Project 30 %
Grading Policy for AACSB Multiple Assessment:
HW Midterms
30%
40%
 Oral Commu./ Presentation
20
10
COMMU
10
 Written Communication
20
CPSI
LEAD
 Creativity and Innovation
 Problem Solving
 Analytical &
Computational Skills
 Leadership & Ethic
 Social responsibility
GLOB
 Global Awareness
VSP
 Values, Skills & Profess.
 Information Technology
 Management Skills
Final
30%
25
25
10
15
20
30
10
10
15
30
10
20
20
Class Web Page:
http://myweb.ncku.edu.tw/_kuojunglee/Teaching/Spring_2013/BayesianAnalysis/Bayesian
Analysis.html
Password to access all files: xxxxxxxx
Office Hours: All office hours will be held in 62427, Management Building.
_ Time: TBA.
Homework:
Homework will be assigned on a weekly basis and it is due in class meeting. No late
homework will be accepted without official excuse. It is possible that only part of the
assigned problems will be graded. It is fine for students to work together on homework
problems, but write-up of the solutions must be done independently. Your understanding of
it will be tested in each test. It is your responsibility to seek help on all problems that you
cannot do. Help is available after class, in the TA office room, xxxxx Management
Building or by appointment with me. Sometimes, I will discuss homework problems in
class.
Tests and Cheat Sheets:
There will be two tests in this course, each test taking 2 hours. You will be allowed to use
one-page of A4 size, two-sided \cheat sheet" for each test. You may also use standard
calculators that do not have external memory drives or data ports. However, calculators that
can be used as communication devices may not be used. You may not use laptops, tablets,
or ipad for any quiz or exam. Wireless communication devices may not be used. If you
have doubts about whether your calculator meets these requirements, contact me. There
will be no make-up for a midterm.
Disability Access Statement: This publication/material, and all other handouts for this
course, are available in alternative formats upon request. Please contact Chi-Ching Wu,
Department of Statistics, 06-2757575 ext. 53633 or wucc@stat.ncku.edu.tw, Management
Building, 2nd Floor.