# 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