Computational Statistics
Course content
- Maximum-likelihood and numerical optimization.
- The EM-algorithm.
- Stochastic optimization algorithms.
- Simulation algorithms and Monte Carlo methods.
- Nonparametric density estimation.
- Bivariate smoothing.
- Numerical linear algebra in statistics. Sparse and structured matrices.
- Practical implementation of statistical computations and algorithms.
- R/C++ and RStudio statistical software development.
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
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.
4 hours of lectures per week for 7 weeks.
2 hours of presentation and discussion of the exam assignments per
week for 7 weeks.
2 hours of exercises per week for 7 weeks.
StatMet and MStat (alternatively MatStat from previous years) or
similar knowledge of statistics and some experience with R usage.
Linear algebra, multivariate distributions, likelihood and least
squares methods are essential prerequisites. It is a good idea to
have a working knowledge of conditional distributions as treated in
Statistics A.
Academic qualifications equivalent to a BSc degree is recommended.
This course requires a certain statistical maturity at the level of
MSc students in statistics. It is not an introduction to R for
statistical data analysis.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Oral examination, 25 minutes
- Type of assessment details
- During the course a total of eight assignments will be
given within four different topics. The student needs to select one
assignment from each topic and prepare a solution of that
assignment for the exam. That is, the student needs to prepare the
solution of four assignments in total.
At the oral exam one assignment out of the four prepared by the student 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. The grade is based on the oral presentation and the following discussion. - Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Two internal examiners.
Criteria for exam assessment
The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.
Single subject courses (day)
- Category
- Hours
- Lectures
- 28
- Preparation
- 119
- Exercises
- 28
- Exam Preparation
- 30
- Exam
- 1
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NMAK16005U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 1
- Schedulegroup
-
A
- Capacity
- No limit
The number of seats may be reduced in the late registration period - Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- Dmytro Marushkevych (8-4f78847f7d7a39784b786c7f73397680396f76)
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Courseinformation of students