Information Package / Course Catalogue
Advanced Biometrical Methods For Quantitative Genetics
Course Code: ZZO626
Course Type: Area Elective
Couse Group: Third Cycle (Doctorate Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

Provide the student with fundamental knowledge about likelihood and Bayesian approaches to solve quantitative problems in animal breeding

Course Content

Probability, Statistical distributions and variables, use of matrices in statistics, Maximum likelihood and Bayesian statistics, Markov Chain Monte Carlo, linear mixed models, Metropolis Hasting and Robust models

Name of Lecturer(s)
Learning Outcomes
1.1. Earning fundamental knowledge about application of statistics
2.2. Learning how to use at least one statistical package
3.3. Earning experiences to apply maximum likelihood and Bayesian approach for parameter estimation
4.4. Earning experiences about developing linear mixed models in animal models
5.5. Earning experiences about Bayesian and Metropolis-Hasting sampling approaches
6.6. Earning experiences about analysis of discrete and continuous variables
7.7. Earning experiences about developing robust models
8.8. Earning knowledge about common statistical methods and programming in animal breeding
Recommended or Required Reading
1.Likelihood, Bayesian, andMCMCMethods in Quantitative Genetics, (2002) Daniel Sorensenand Daniel Gianola, Springer
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
Probability theory and discrete and continuous distributions
Week 2 - Theoretical & Practice
Variance-covariance of random variables
Week 3 - Theoretical & Practice
The use matrices in statistics
Week 4 - Theoretical & Practice
Multivariate normal distributions
Week 5 - Theoretical & Practice
Introduction to maximum likelihood
Week 6 - Theoretical & Practice
Characteristics of maximum likelihood estimations
Week 7 - Theoretical & Practice
Characteristics of maximum likelihood estimations
Week 8 - Theoretical & Practice
Maximum likelihood estimation of parameters for discrete data
Week 9 - Theoretical & Practice
Bayesian Statistics
Week 10 - Theoretical & Practice
Hierarchical Bayesian model and Emprical Bayes analysis
Week 11 - Theoretical & Practice
Markov Chain Monte Carlo
Week 12 - Theoretical & Practice
Linear mixed models
Week 13 - Theoretical & Practice
Threshold models and MCMC
Week 14 - Theoretical & Practice
Metropolis-Hasting sampling
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%30
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142256
Lecture - Practice142256
Term Project141242
Midterm Examination118220
Final Examination124226
TOTAL WORKLOAD (hours)200
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
OÇ-1
2
1
OÇ-2
2
2
2
OÇ-3
2
1
2
OÇ-4
2
2
1
OÇ-5
1
1
2
OÇ-6
1
1
OÇ-7
1
1
2
OÇ-8
2
2
1
1
Adnan Menderes University - Information Package / Course Catalogue
2026