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 corequisities 

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 

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   

Recommended or Required Reading 

Planned Learning Activities and Teaching Methods 

Assessment Methods and Criteria   Language of Instruction   Work Placement(s)  

Workload Calculation 