Information Package / Course Catalogue
Simulation Methods in Biostatistics
Course Code: BİS537
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 2
Credit: 4
Lab: 0
ECTS: 6
Objectives of the Course

The aim of this course is to teach students the ability to model, simulate and analyse complex systems in a reasonable time.

Course Content

Random number generation and random distributions, Monte Carlo simulations, Markov chain theory, Monte Carlo Markov chain method, bootstrap and Jackknife methods, the Gibbs and Metropolis algorithms, simulation of medical data and application.

Name of Lecturer(s)
Prof. İmran KURT ÖMÜRLÜ
Learning Outcomes
1.Be able to do programming
2.Be able to numerify of biological systems
3.Learn the techniques of estimate coeeficients using optimization
4.Be able to entegrate statistics to simulation techniques
5.To be able to apply simulation techniques
Recommended or Required Reading
Weekly Detailed Course Contents
Week 1 - Theoretical
Generating uniform random samples
Week 1 - Practice
Application with package programs
Week 2 - Theoretical
Statistical tests for randomness
Week 2 - Practice
Application with package programs
Week 3 - Theoretical
Generating non-uniform random samples
Week 3 - Practice
Application with package programs
Week 4 - Theoretical
Random distributions
Week 4 - Practice
Application with package programs
Week 5 - Theoretical
Monte Carlo Simulations
Week 5 - Practice
Application with package programs
Week 6 - Theoretical
Markov Chain Theory
Week 6 - Practice
Application with package programs
Week 7 - Theoretical
Monte Carlo-Markov Chain Method-1
Week 7 - Practice
Application with package programs
Week 8 - Intermediate Exam
Midterm exam
Week 9 - Theoretical
Monte Carlo-Markov Chain Method-2
Week 9 - Practice
Application with package programs
Week 10 - Theoretical
Bootstrap and Jackknife Methods-1
Week 10 - Practice
Application with package programs
Week 11 - Theoretical
Bootstrap and Jackknife Methods-2
Week 11 - Practice
Application with package programs
Week 12 - Theoretical
Gibbs and Metropolis algorithms-1
Week 12 - Practice
Application with package programs
Week 13 - Theoretical
Gibbs and Metropolis algorithms-2
Week 13 - Practice
Application with package programs
Week 14 - Theoretical
Simulation of medical data
Week 14 - Practice
Application with package programs
Week 15 - Theoretical
Literature review and discussion
Week 15 - Practice
Literature review and discussion
Week 16 - Final Exam
Final exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140342
Lecture - Practice140228
Assignment110010
Quiz72121
Midterm Examination120222
Final Examination120222
TOTAL WORKLOAD (hours)145
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
4
4
4
3
4
3
3
4
3
4
OÇ-2
4
4
4
4
3
3
3
2
2
3
OÇ-3
2
3
3
3
3
3
3
3
2
2
OÇ-4
5
5
5
4
5
4
4
4
5
5
OÇ-5
Adnan Menderes University - Information Package / Course Catalogue