GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
BAYESIAN STATISTICS/İST4030
Course Title: BAYESIAN STATISTICS
Credits 3 ECTS 4
Course Semester 8 Type of The Course Elective
COURSE INFORMATION
 -- (CATALOG CONTENT)
 -- (TEXTBOOK)
 -- (SUPPLEMENTARY TEXTBOOK)
 -- (PREREQUISITES AND CO-REQUISITES)
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- COURSE OBJECTIVES
 -- COURSE LEARNING OUTCOMES
Bayesian approach to statistics.
Prior information and its description, Conjugate families of distributions.
Bayesian inference for Discrete random variables.
Bayesian inference for Continuous random variables.

 -- MODE OF DELIVERY
  The mode of delivery is face to face.
 --WEEKLY SCHEDULE
1. Week  What is the Bayesian approach to statistics? How does it differ from the frequentist approach? Some features of Bayesian inference.
2. Week  Review of Probability Theory, Sample space, Conditional probabilities, Bayes Theorem, Prior information.
3. Week  Bayesian inference for discrete random variables with discrete prior: Bernoulli Probability Models.
4. Week  Bayesian inference for discrete random variables with discrete prior: Poisson Probability Models.
5. Week  Bayesian inference for Binomial Probability models with continuous prior distribution. Conjugate family for Binomial observation: Beta-Binom model.
6. Week  Comparing Bayesian and frequentist inference for Binomial probability model.
7. Week  Bayesian inference for Poisson Probability models with continuous prior distribution. Conjugate family for Poisson observation: Gamma-Poisson model.
8. Week  Bayesian inference for the mean of the Normal Probability models with discrete prior. Choosing the prior, summarizing the distribution. Midterm
9. Week  Bayesian inference for the mean of the Normal Probability models with continuous prior distribution. Choosing the prior, summarizing the results.
10. Week  Bayesian inference for the mean of the Normal Probability models with non normal continuous prior distribution.
11. Week  Bayesian Credible interval for the mean of the Normal Probability Models with known variance and unknown variance.
12. Week  Comparing Bayesian and frequentist inferences for the mean of the Normal Probability Model.
13. Week  Bayesian inference for the variance of the Normal Probability models with continuous prior distribution. Choosing the prior, summarizing the results.
14. Week  Bayesian inference for the standard deviation of the Normal Probability models with continuous prior distribution. Choosing the prior.
15. Week  Final Exam
16. Week  __
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
40
 Assignment
0
0
 Application
0
0
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Percent of In-term Studies  
40
 Percentage of Final Exam to Total Score  
60
 -- WORKLOAD
 Activity  Total Number of Weeks  Duration (weekly hour)  Total Period Work Load
 Weekly Theoretical Course Hours
14
3
42
 Weekly Tutorial Hours
0
 Reading Tasks
7
3
21
 Searching in Internet and Library
4
3
12
 Material Design and Implementation
0
 Report Preparing
0
 Preparing a Presentation
0
 Presentation
0
 Midterm Exam and Preperation for Midterm Exam
5
2
10
 Final Exam and Preperation for Final Exam
5
3
15
 Other (should be emphasized)
0
 TOTAL WORKLOAD: 
100
 TOTAL WORKLOAD / 25: 
4
 Course Credit (ECTS): 
4
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
PROGRAM LEARNING OUTCOMES
1
2
3
4
5
11. The statistical textbooks which include latest information about statistics, equipment and other resources supported by scientific approach on undergraduate level have theoretical and practical knowledge.X
22. Statisticians by using knowledge and skills acquired at bachelor degree level model, analyze, and interpret datasets.X
33. Statisticians identify and analyze the problems with current developments in statistic and also develop solutions based upon researches and proofs.X
44. Statisticians apply theoretical and practical knowledge acquired in Statistics at bachelor degree level to the current problems.X
55. Statisticians have the ability to use computer software and computing technology at the certain level required by statistics field.X
66. Statisticians take responsibility at disciplinary and interdisciplinary studies as an individual or a team member.X
77. Statisticians must have knowledge and ability to follow development in the field of Statistics, and must develop life long-learning attitudes.X
88. By using a foreign language, statistician can keep track of every statistical information, and communicate with colleagues.X
99. Applying the statistical knowledge in the professional sense, statistician has social, scientific, and ethical values.X
1010. A statistician must have the ability to social sensitivity and socialization.X
1111. During the process of inference, a statistician uses time efficiently with the analytical thinking ability.X
 -- NAME OF LECTURER(S)
   (Assoc. Prof. Necla GÜNDÜZ)
 -- WEB SITE(S) OF LECTURER(S)
   (https://websitem.gazi.edu.tr/site/ngunduz)
 -- EMAIL(S) OF LECTURER(S)
   (ngunduz@gazi.edu.tr)