GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
BAYESIAN STATISTICS/İST-430
Course Title: BAYESIAN STATISTICS
Credits 3 ECTS 5
Semester 8 Compulsory/Elective Elective
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Assist. Prof. Necla GÜNDÜZ
 -- WEB SITE(S) OF LECTURER(S)
  http://websitem.gazi.edu.tr/site/ngunduz
 -- EMAIL(S) OF LECTURER(S)
  ngunduz@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
Bayes Theorem
Bayesian analyssis for discrete random variables
Bayesian analyssis for continous random variables
Obtaining Bayesian estimators





 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 -- PREREQUISITES AND CO-REQUISITES
  There is no prerequisite or co-requisite for this course
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  There is no recommended optional programme component for this course.
 --COURSE CONTENT
1. Week  Basic probability concepts, subjective and frequency interpretation of probability
2. Week  Some special probability distributions (Bernouilli, Binom, Negatif Binomial, Hypergeometric, Poisson, Multinomial, K Discrete Uniform)
3. Week  Some special probability distributions (Normal, exponential, gamma, beta, continous uniform)
4. Week  Conditional probability for random variables and Bayes Theorem, Comparing of Bayesian inference and classical inference
5. Week  Bayes theorem for discrete random variables, Discrete prior distributions: Bernouilli example
6. Week  Discrete prior distributions: Poisson example
7. Week  Using the Bayes theorem sequentially
8. Week  Mid term exam
9. Week  Bayes theorem for continous random variables: Conjugate prior distributions for the Bernouilli Process, The use of Beta prior distrivution, beta-binom
10. Week  Point estimation
11. Week  Interval estimation: credible interval
12. Week  Hypotesis testing
13. Week  Conjugate prior distributions for normal process,the use of normal prior distributions,normal-normal model
14. Week  Identification of prior distribution with Jeffrey rule(Jeffrey rule for one or more than one parameter)
15. Week  Bayesian approach for linear regression models and example
16. Week  Final Exam
 -- RECOMMENDED OR REQUIRED READING
  Winkler, R. L., 1972, An Introduction to Bayesian Inference and Decision, Holt, Rinehart and Winston, Inc., USA. Bolstad, W. M., 2007, Introduction
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
   Lecture, Question & Answer, Demonstration, Drill - Practise
 -- WORK PLACEMENT(S)
  No
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
1
40
 Assignment
0
0
 Exercises
0
0
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
40
 Contribution of Final Examination to Overall Grade  
60
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
1
2
2
 Practising Hours of Course Per Week
1
2
2
 Reading
14
3
42
 Searching in Internet and Library
0
0
 Designing and Applying Materials
0
0
 Preparing Reports
14
3
42
 Preparing Presentation
0
 Presentation
0
 Mid-Term and Studying for Mid-Term
1
15
15
 Final and Studying for Final
1
22
22
 Other
0
 TOTAL WORKLOAD: 
125
 TOTAL WORKLOAD / 25: 
5
 ECTS: 
5
 -- 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