GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
MONTE CARLO SIMULATION/6321303
Course Title: MONTE CARLO SIMULATION
Credits 3 ECTS 7.5
Semester 1 Compulsory/Elective Elective
COURSE INFO
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- NAME OF LECTURER(S)
  Prof.Dr.Mustafa Y. Ata
 -- WEB SITE(S) OF LECTURER(S)
  http://websitem.gazi.edu.tr/site/myata; http://istatistikseliletisim.net
 -- EMAIL(S) OF LECTURER(S)
  myata@gazi.edu.tr
 -- LEARNING OUTCOMES OF THE COURSE UNIT
• Knowledge of a collection of simulation methods including Markov chain Monte Carlo (MCMC); understanding of Monte Carlo procedures, their advantages
• Ability to develop and implement (in BUGS) an MCMC algorithm for a given probability distribution.
• Ability to implement an MCMC algorithm in a programming language such as FORTRAN or C for a well-defined problem in a scientific application.
• Ability to use Monte Carlo methods for scientific applications.
• Ability to evaluate a stochastic simulation algorithm with respect to both its efficiency and the validity of the inference results produced by it.
 -- MODE OF DELIVERY
   The mode of delivery of this course is Face to face
 -- PREREQUISITES AND CO-REQUISITES
  • A good working knowledge of a scientific programming language. • A basic background in Probability and Statistics. • A basic knowledge of the statistical programming language R.
 -- RECOMMENDED OPTIONAL PROGRAMME COMPONENTS
  5331301 Simulation Techniques.
 --COURSE CONTENT
1. Week  Introduction: Virtual and actual world.
2. Week  Scientific Modelling: Systems, random variables as inputs and outputs of a system, parameters vs statistics.
3. Week  Monte Carlo Estimation: Direct estimation of parameters via the Law of Large Numbers.
4. Week  General Methods of Generating Virtual Observations: Inverse transformation technique, acceptance-rejection technique.
5. Week  Generating Pseudo Random Numbers
6. Week  Random Sampling from Some Special Probability Distributions
7. Week  Midterm Exam.
8. Week  Some Efficiency Methods in Designing Virtual Experiments: Antithetic variables, control variates, conditioning, importance sampling.
9. Week  Fundamentals of Bayesian Inference: Bayesian vs frequentist approach in statistical inference, prior and posterior distributions.
10. Week  Fundamentals of Markov Chain Theory: Convergence of Markov chains, detailed balance, limit theorems, ergodic Markov processes.
11. Week  Random Sampling from Any Multidimensional Distribution: Metropolis-Hastings algorithm and Gibbs sampler.
12. Week  Sequential Monte Carlo Methods: Sequential importance sampling, sequential imputation, the bootstrap techniques.
13. Week  Implementational issues: Burn In, Convergence diagnostics, Monte Carlo error.
14. Week  More Advanced Algorithms: Auxiliary variable methods, simulated and parallel tempering, simulated annealing; reversible jump MCMC, EM algorithm
15. Week  Applications of Monte Carlo.
16. Week  Final Exam.
 -- RECOMMENDED OR REQUIRED READING
  • W.R. Gilks, S.,Richardson, and D.J. Spiegelhalter (eds.) Markov chain Monte Carlo in practice, London : Chapman & Hall, 1996 • Christian P. Robert and George Casella Introducing Monte Carlo Methods with R, New York: Springer, 2010. • Sheldon M. Ross, Simulation, 5th ed., San Diego, CA:Academic Press, 2013.
 -- PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
  Lecture, Question & Answer, Demonstration, Drill - Practise
 -- WORK PLACEMENT(S)
  -
 -- ASSESSMENT METHODS AND CRITERIA
 
Quantity
Percentage
 Mid-terms
1
20
 Assignment
8
10
 Exercises
0
0
 Projects
1
10
 Practice
0
0
 Quiz
0
0
 Contribution of In-term Studies to Overall Grade  
40
 Contribution of Final Examination to Overall Grade  
60
 -- WORKLOAD
 Efficiency  Total Week Count  Weekly Duration (in hour)  Total Workload in Semester
 Theoretical Study Hours of Course Per Week
14
3
42
 Practising Hours of Course Per Week
0
0
0
 Reading
14
4
56
 Searching in Internet and Library
14
1
14
 Designing and Applying Materials
8
3
24
 Preparing Reports
8
3
24
 Preparing Presentation
2
3
6
 Presentation
2
1
2
 Mid-Term and Studying for Mid-Term
1
10
10
 Final and Studying for Final
1
10
10
 Other
0
 TOTAL WORKLOAD: 
188
 TOTAL WORKLOAD / 25: 
7.52
 ECTS: 
7.5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
11. Based on the capabilities of undergraduate level, the students enrolled to the program can develop and deepen their knowledge and skill at the level of expertise on the same field of the undergradute study or a different field.X
22. The students use their theoretical and practical knowledge at the level of expertise in the area of statistics.X
33. The students should evaluate their acquired knowledge and skills in a critical perspective and the critical point of view guides their learning process.X
44. Theoretical and practical knowledge gained in graduate level in the field of Statistics should be applied and transfer to the current problems.X
55. By performing the process from the identification of the scientific research problem to reporting and the process should be transferred in oral, written and visual ways.X
66. The students should use computer software and information technologies on the level required by the field of Statistics.X
77. The students should have the ability to use Statistics in interdisciplinary studies.X
88. The students should have enough foreign language level to pursue statistical literature.X
99. At the required level of field of statistics, he/she should use statistical software and information technology efficiently in a such a way that helps solving problems in his/her research.X
1010. In the process of applying knowledge in a professional sense, social, scientific, and ethical values should be regarded.X