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Dansk English

ST522: Computational Statistics (10 ECTS)

STADS: 25003501

Level
Bachelor course

Teaching period
The course is offered in the spring semester.

Teacher responsible
Email: yuri.goegebeur@stat.sdu.dk

Timetable
Group Type Day Time Classroom Weeks Comment
Common I Tuesday 12-14 U156 6,9-10
Common I Tuesday 08-10 U56 7,17
Common I Tuesday 12-14 U155 8,11-13,16,18
Common I Tuesday 12-14 U20 19-21
Common I Wednesday 08-10 U155 12,21
Common I Thursday 10-12 U25A 6
Common I Thursday 10-12 U155 7-9,19-20
Common I Thursday 10-12 U143 10
Common I Thursday 12-14 U142 11
Common I Thursday 10-12 U23A 13,16-17
Common I Thursday 10-12 U20 18
H1 TE Tuesday 12-14 U156 7
H1 TE Tuesday 08-10 U146 8
H1 TE Tuesday 08-10 U56 9-11
H1 TL Wednesday 08-10 U155 19
H1 TL Thursday 14-16 U10 18
H1 TL Friday 10-12 U24 6
H1 TL Friday 10-12 U17 7
H1 TL Friday 10-12 U31 8-9,20
H1 TL Friday 10-12 U143 10
H1 TL Friday 10-12 U142 11
H1 TL Friday 10-12 U23A 12,16-17,21
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Comment:
Ubegrænset deltagerantal. Fælles undervisning med ST816

Prerequisites:
None

Academic preconditions:
Students taking the course are expected to:
  • Have knowledge of mathematical statistics.


Course introduction
The aim of the course is to enable the student to use modern computer intensive statistical methods as tools to investigate stochastic phenomena and statistical procedures, and to perform statistical inference, which is important in regard to conducting statistical analysis based on computation and simulation.

The course builds on the knowledge acquired in the courses calculus and mathematical statistics, and gives an academic basis for studying the topics probability theory, order statistics and extreme value statistics, that are part of the degree.

In relation to the competence profile of the degree it is the explicit focus of the course to:

  • Give the competence to handle model building and/or model calculations.
  • Give skills to perform statistical analyses.
  • Give theoretical knowledge about and practical experience with the application of methods and models in statistics.


Expected learning outcome
The learning objective of the course is that the student demonstrates the ability to:
  • Reproduce key theoretical results concerning elementary operations on random variables and vectors, and to apply these to simple theoretical assignments.
  • Reproduce and apply the fundamental theorems of random variate generation.
  • Simulate variates and vectors from the most common distributions.
  • Evaluate the quality of a random number generator.
  • Apply the basic principles of variance reduction.
  • Simulate complex systems and investigate their properties.
  • Use simulation to approximate integrals.
  • Use simulation to compute p-values and confidence intervals.
  • Investigate properties of statistical procedures and estimators using simulation.
  • Perform programming relevant to the content of the course in the statistical package used in the course.
  • Identify and interpret relevant information in the output of the statistical package used in the course.
  • Summarize the results of an analysis in a statistical report.
Subject overview
The following main topics are contained in the course:
Random number generators, inversion method, rejection sampling, simulation from multivariate distributions, Markov Chain Monte Carlo methods, permutation and randomization tests, transformations, simulation of experiments and complex systems, Monte Carlo integration, simulation of stochastic processes, bootstrap methods, Bayesian models and methods, EM algorithm, nonparametric density estimation.
 


Literature
    Meddeles ved kursets start


Website
This course uses e-learn (blackboard).

Prerequisites for participating in the exam
None

Assessment and marking:
  1. The first part of the course is evaluated by projects. Danish 7 point scale, internal censorship. (5 ECTS). (25003512).
  2. The second part of the course is evaluated at an oral exam. Danish 7 point scale, internal censor. The oral exam is based on, but not limited to, projects made during the second half of the course. (5 ECTS). (25003502).
Expected working hours
The teaching method is based on three phase model.
Intro phase: 56 hours
Skills training phase: 36 hours, hereof:
 - Tutorials: 10 hours
 - Laboratory exercises: 26 hours

Educational activities
Studying the course material and preparing the weekly exercises, individually or through group work.Educational form
In the intro phase a modified version of the classical lecture is employed, where the terms and concepts of the topic are presented, from theory as well as from examples based on actual data. In these hours there is room for questions and discussions. In the training phase the students work with data-based problems and discussion topics, related to the content of the previous lectures in the intro phase. In these hours there is a possibility of working specifically with selected difficult concepts. In the study phase the students work independently with problems and the understanding of the terms and concepts of the topic. Questions from the study phase can afterwards be presented in either the intro phase hours or the training phase hours.

Language
This course is taught in Danish or English, depending on the lecturer. However, if international students participate, the teaching language will always be English.

Remarks
The course is co-read with ST816.

Course enrollment
See deadline of enrolment.

Tuition fees for single courses
See fees for single courses.