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Computational statistics, 6 ECTS Credits
COURSE CATEGORY   Master´s Programme in Statistics, Data Analysis and Knowledge Discovery
  COURSE CODE   732A38
After completing the course the students shall be able to:
- show knowledge about powerful techniques for simulation and sensitivity analysis,
- demonstrate a basic understanding of major numerical principles for the fitting of statistical models to data.
- carry out computer experiments involving Monte-Carlo techniques, i.e. the use of random number generation to simulate stochastic phenomena and model outputs.
- adapt general principles of computing to specific statistical applications involving linear systems of equations.
The course aims at enabling insightful selection of computational tools and algorithms in statistics.

The course lays the foundation for professional work and research in which advanced computation and computer experiments involving simulation are employed to make inference about data and the performance of statistical methods.

Basic principles of random number generation and simulation. Markov Chain Monte Carlo (MCMC) simulation. Variance based sensitivity analysis of model outputs. Numerical linear algebra and optimization for fitting of statistical models to data.
The teaching comprises lectures and computer exercises. The lectures are devoted to presentations of theories, concepts, and methods. Computer exercises in which the students have access to supervision provide practical experience of data analysis. Literatur readings. Language of instruction: English
Assignments encompassing computer-based data analysis. One final written examination.

Students failing an exam covering either the entire course or part of the course two times are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.

Students entering the course shall have taken at least one course at advanced level in statistics and be familiar with linear statistical models, including multiple regression. Also, it is a prerequisite that the students have taken courses in calculus and linear algebra, and have experience of programming for statistical data analysis.
The course is graded according to the ECTS grading scale A-F
Course certificate is issued by the Faculty Board on request. The Department provides a special form which should be submitted to the Student Affairs Division.
The course literature is decided upon by the department in question.
Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus.

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.
Computational statistics
Computational statistics
Department responsible
for the course or equivalent:
IDA - Department of Computer and Information
Registrar No: 1330/06-41   Course Code: 732A38      
    Exam codes: see Local Computer System      
Subject/Subject Area : Statistik - STA          
Level   Education level     Subject Area Code   Field of Education  
A1X   Advanced level     STA   SA  
The syllabus was approved by the Board of Faculty of Arts and Science 2008-09-11