ST816: Computational Statistics (10 ECTS)
STADS: 25003601
Level
Master's level course approved as PhD course
Teaching period
The course is offered in the spring semester.
Teacher responsible
Email: yuri.goegebeur.imada.sdu.dk
Timetable
Group |
Type |
Day |
Time |
Classroom |
Weeks |
Comment |
Common |
I |
Monday |
08-10 |
U154 |
17 |
|
Common |
I |
Tuesday |
08-10 |
U155 |
6-12 |
|
Common |
I |
Tuesday |
08-10 |
U23A |
15-17,19-21 |
|
Common |
I |
Wednesday |
12-14 |
U141 |
12 |
|
Common |
I |
Thursday |
12-14 |
U64 |
6-7 |
|
Common |
I |
Thursday |
12-14 |
U146 |
8 |
|
Common |
I |
Thursday |
10-12 |
U31 |
8-12,15-16,18,21 |
|
Common |
I |
Thursday |
10-12 |
U154 |
20 |
|
Common |
I |
Friday |
10-12 |
U154 |
18 |
|
H1 |
TE |
Wednesday |
12-14 |
U64 |
7-11 |
|
H1 |
TL |
Wednesday |
12-14 |
U8 |
17 |
|
H1 |
TE |
Wednesday |
12-14 |
U153 |
20 |
|
H1 |
TL |
Friday |
10-12 |
U154 |
6-7,9-12,16,19-21 |
|
H1 |
TL |
Friday |
10-12 |
U64 |
8 |
|
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Comment:
Ubegrænset deltagerantal. Fælles undervisning med ST522
Prerequisites:
None
Academic preconditions:
Academic preconditions. Students taking the course are expected to:
- Have knowledge of mathematical statistics.
Course introductionThe 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 outcomeThe 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 overviewThe 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.
LiteratureMeddeles ved kursets start
Website
This course uses
e-learn (blackboard).
Prerequisites for participating in the exam
None
Assessment and marking:
- The first part of the course is evaluated by projects. Grading after 7 point scale, internal censor. (5 ECTS). (25003612).
- The second part of the course is evaluated at an oral exam. The oral exam is based on, but not limited to, projects made during the second half of the course. Grading after 7 point scale, internal censor. (5 ECTS) (25003602).
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
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 ST522.
The course cannot be chosen by students who have passed ST522.
Course enrollment
See deadline of enrolment.
Tuition fees for single courses
See fees for single courses.