Forudsætninger/Anbefalede forudsætninger for at deltage i modulet

The students completing the module will have gained knowledge about computational statistics and a number of topics where methods from computational statistics play an important part in the topic. The list may include, but is not limited to, the following topics:

Modulets indhold, forløb og pædagogik

Læringsmål

Viden

• Quantitative software development

• Implemention of numerical algorithms

• Monte Carlo foundations and applications

• Relational databases and their usage

• optimization of discrete problems including heuristics and meta-heuristics

• Stochastic differential equations (SDEs) and examples of their application

• Numerical treatment of (SDEs) and partial differential equations (PDEs)

Færdigheder

• can apply the relevant methodologies in one or more scenarios by using appropriate software implementations, and interpret the output and modify the parameters accordingly

• are able state the underlying assumptions and argue about limitations and extendibility of the methodology in one or more specific settings

• can assess goodness-of-fit for the models where appropriate

Kompetencer

• can acquire supplementary knowledge about the relevant methodologies

• can combine appropriate topics from the course to analyse a specific problem

• can in writing describe the methodologies, results and outcome from an analysis of a specific problem

Undervisningsform

Lectures with exercises.

Omfang og forventet arbejdsindsats

The module is 5 ECTS equivalent to a workload of 137,5 hours.

Eksamen

Prøver

Prøvens navn	Computational Statistics and Applications
Prøveform	Skriftlig eller mundtlig Skriftlig eller mundtlig eller løbende evaluering
ECTS	5
Bedømmelsesform	Bestået/ikke bestået
Censur	Intern prøve
Vurderingskriterier	Vurderingskriterierne er angivet i Universitetets eksamensordning

Yderligere informationer

Please contact Study Board of Mathematical Sciences - studyboard@math.aau.dk