Description of Individual Course Units
Course Unit CodeCourse Unit TitleType of Course UnitYear of StudySemesterNumber of ECTS Credits
İST417BAYESIAN STATISTICSElective475
Level of Course Unit
First Cycle
Objectives of the Course
The aim of this course is to give basic and important concepts about Bayesian statistical methods and to make Bayesian statistics calculations with WinBUGS software.
Name of Lecturer(s)
Learning Outcomes
1To be able to express the differences between classical statistics and Bayesian statistical methods.
2To be able to obtain posterior distribution due to a given a priori distribution
3To be able to extract the Bayesian estimator of a given mass parameter
4To be able to calculate Bayesian confidence intervals
5To be able to do Bayesian hypothesis test
6To be able to make Bayesian statistics calculations with WinBUGS.
Mode of Delivery
Face to Face
Prerequisites and co-requisities
Recommended Optional Programme Components
Course Contents
Probability, conditional probability, Bayes rule, parameter and prior distribution, posterior distribution, Bayesian estimator, random variables, likelihood function, displaceability, Finetti theorem, some preliminary distributions for posterior inference, Normal model, Bayesian confidence intervals, Bayesian hypothesis tests, Monte Carlo method, Markov Chain Monte Carlo (MCMC) method. Structure and basic components of WinBUGS software, Bayesian model building in WinBUGS, problem solving with WinBUGS.
Weekly Detailed Course Contents
WeekTheoreticalPracticeLaboratory
1Probability, conditional probability, Bayes rule
2Parameter and prior distribution
3Posterior distribution
4Bayesian estimator
5Random variables, likelihood function
6Interchangeability, Finetti theorem
7Some preliminary distributions for posterior inference
8Some preliminary distributions for posterior inferenc
9MİDTERM EXAM
10Normal Model
11Bayesian confidence intervals
12Bayesian hypothesis tests
13Bayesian linear regression
14Monte Carlo method, Markov Chain Monte Carlo (MCMC) methods and applications
15Monte Carlo method, Markov Chain Monte Carlo (MCMC) methods and applications
16FİNAL EXAM
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
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Language of Instruction
Work Placement(s)
Workload Calculation
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Contribution of Learning Outcomes to Programme Outcomes
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* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High
 
Ege University, Bornova - İzmir / TURKEY • Phone: +90 232 311 10 10 • e-mail: intrec@mail.ege.edu.tr