Kursussøgning, efter- og videreuddannelse – Københavns Universitet

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Kursussøgning, efter- og videreuddannelse

Computational Statistics

Practical information
Study year 2016/2017
Block 1
Programme level Full Degree Master
Course responsible
  • Niels Richard Hansen (14-706b676e753074306a6370756770426f63766a306d7730666d)
  • Department of Mathematical Sciences
Course number: NMAK16005U

Course 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


  • 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 prerequisites

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.

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MSc Programme in Statistics
MSc Programme in Mathematics-Economy


Study Board of Mathematics and Computer Science

Course type

Single subject courses (day)


1 block


---- SKEMA LINK ----

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.


No restrictions/ no limitations




Category Hours
Lectures 28
Exercises 14
Exam 1
Exam Preparation 30
Preparation 133
English 206


Type of assessment

Oral examination, 25 min
During 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.

Marking scale

7-point grading scale

Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

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 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.

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