          Description of Individual Course Units
 Course Unit Code Course Unit Title Type of Course Unit Year of Study Semester Number of ECTS Credits İST417 BAYESIAN STATISTICS Elective 4 7 5
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
 1 To be able to express the differences between classical statistics and Bayesian statistical methods. 2 To be able to obtain posterior distribution due to a given a priori distribution 3 To be able to extract the Bayesian estimator of a given mass parameter 4 To be able to calculate Bayesian confidence intervals 5 To be able to do Bayesian hypothesis test 6 To 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
 Week Theoretical Practice Laboratory 1 Probability, conditional probability, Bayes rule 2 Parameter and prior distribution 3 Posterior distribution 4 Bayesian estimator 5 Random variables, likelihood function 6 Interchangeability, Finetti theorem 7 Some preliminary distributions for posterior inference 8 Some preliminary distributions for posterior inferenc 9 MİDTERM EXAM 10 Normal Model 11 Bayesian confidence intervals 12 Bayesian hypothesis tests 13 Bayesian linear regression 14 Monte Carlo method, Markov Chain Monte Carlo (MCMC) methods and applications 15 Monte Carlo method, Markov Chain Monte Carlo (MCMC) methods and applications 16 FİNAL EXAM 