NMAK16005U Computational Statistics
MSc Programme in Statistics
MSc Programme in Mathematics-Economy
- Maximum-likelihood and the EM-algorithm.
- Simulation algorithms and Monte Carlo methods.
- Markov Chain Monte Carlo.
- Univariate and multivariate smoothing.
- Numerical linear algebra in statistics. Sparse and structured matrices.
- Practical implementation of statistical computations and algorithms.
- R/C/C++ and RStudio statistical software development.
- fundamental algorithms for statistical computations
- 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.
2 hours of presentation and discussion of a weekly assignment per week for 7 weeks.
- 7,5 ECTS
- Type of assessment
- Oral examination, 25 minutesDuring the course a total of eight assignments will be given. Four of these must be solved, and at the oral exam one assignment out of the four is selected at random for presentation by the student. The oral exam is without preparation. The presentation is followed by a discussion with the examinator within the topics of the course.
- Exam registration requirements
To participate in the final oral exam one oral presentation must have been given during the course.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Two internal examiners.
Same as ordinary exam. To be eligible for the re-exam, students who did not give an oral presentation during the course must hand in synopses of the solutions of four assignments no later than two weeks before the beginning of the re-exam week. The four synopses must be approved in order to take the re-exam.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
- Exam Preparation