NMAK15020U Statistical Computing
Volume 2015/2016
Education
MSc programme in Statistics
MSc programme in Mathematics-Economy
Content
The course will be on computational aspects of data analysis and cover the following topics:
- Likelihood computations.
- Numerical MLE. Smooth optimization. The EM-algorithm and variations.
- Simulation algorithms. MCMC and bootstrapping.
- Numerical linear algebra in statistics. Sparse and structured matrices.
- Univariate and multivariate smoothing.
- Practical implementation of statistical computations and algorithms.
- R/C/C++ and RStudio statistical software development.
Learning Outcome
Knowledge:
- algorithms for statistical data analysis
- R packages that implement some of these algorithms
- or are useful for developing novel implementations.
Skills: Ability to
- implement
- test
- debug
- benchmark
- profile
- and optimize statistical software
Competences: Ability to
- select appropriate numerical algorithms for statistical computations
- evaluate implementations in terms of correctness, robustness, accuracy and memory and speed efficiency.
Academic qualifications
Statistik 2 or similar
knowledge of statistics. Linear algebra and the multivariate normal
distribution are essential prerequisites. Some experience with R
usage.
Teaching and learning methods
4 hours of lectures per week
for 7 weeks.
2 hour presentation and discussion of a weekly assignment.
2 hour presentation and discussion of a weekly assignment.
Remarks
Every week an assignment on
the implementation of a solution to a statistical computing problem
will be given. Students will in turn present solutions in class
followed by a plenary discussion of the solutions. The
student's own solutions will form the basis for his or her oral
examination.
Workload
- Category
- Hours
- Exercises
- 14
- Lectures
- 28
- Preparation
- 164
- Total
- 206
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Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Oral examination, 25 minDuring the course there will be a total of 6 weekly assignments. Each student prepares a presentation of the solutions during the course. At the oral exam a random selection of one of the 6 assignments must be presented without preparation. Based on the presentation the student then has to participate in a discussion with the examinator within the topics of the course.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NMAK15020U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- B
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Niels Richard Hansen (14-746f6b72793478346e6774796b744673677a6e34717b346a71)
Saved on the
27-04-2015