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Computational Statistics
Provider: Faculty of Science

Activity no.: 5544-19-07-31 
Enrollment deadline: 02/09/2019
PlaceDepartment of Mathematical Sciences
Universitetsparken 5, 2100 København Ø
Date and time02.09.2019, at: 09:00 - 10.11.2019, at: 16:00
Regular seats50
ECTS credits7.50
Contact personNina Weisse    E-mail address: weisse@math.ku.dk
Enrolment Handling/Course OrganiserNiels Richard Hansen    E-mail address: niels.r.hansen@math.ku.dk
Written languageEnglish
Teaching languageEnglish
Semester/BlockBlock 1
Scheme groupC
Exam formOral examination
Exam formOral examination, 25 min
Exam detailsDuring 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.
Course workload
Course workload categoryHours
Lectures28.00
Exercises28.00
Exam1.00
Eksamensforberedelse30.00
Preparation119.00
Sum206.00
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.

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.

Teaching and learning methods
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.

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