NMAK16005U Computational Statistics
Volume 2016/2017
Education
MSc Programme in Statistics
MSc Programme in Mathematics-Economy
Content
- 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.
Learning Outcome
Knowledge:
- 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.
Recommended Academic Qualifications
Statistik 2 or similar
knowledge of statistics and some experience with R usage. Linear
algebra, multivariate distributions, likelihood and least squares
methods are essential prerequisites.
Teaching and learning methods
4 hours of lectures per week
for 7 weeks.
2 hours of presentation and discussion of a weekly assignment per week for 7 weeks.
2 hours of presentation and discussion of a weekly assignment per week for 7 weeks.
Workload
- Category
- Hours
- Exam
- 1
- Exam Preparation
- 30
- Exercises
- 14
- Lectures
- 28
- Preparation
- 133
- Total
- 206
Sign up
Self Service at KUnet
As
an exchange, guest and credit student - click here!
Continuing Education - click here!
Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Oral examination, 25 minDuring the course a total of 6 assignments will be given. At the oral exam one of the 6 assignments is selected at random and the student presents it 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.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Two internal examiners.
- Re-exam
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 to all 6 assignments no later than 2 weeks before the beginning of the re-exam week. The 6 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.
Course information
- Language
- English
- Course code
- NMAK16005U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 1
- Schedule
- C
- Course capacity
- No restrictions/ no limitations
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Niels Richard Hansen (14-76716d747b367a367069767b6d764875697c7036737d366c73)
Saved on the
09-03-2016