Topics in Statistical Sciences I

2018/2019

Content, progress and pedagogy of the module

Learning objectives

Knowledge

  • dynamical linear models, including the Kalman filter 
  • population methods, specifically evolutionary computing and genetic algorithms
  • meta analysis 
  • robust statistical methods including non-parametric models
  • factor analysis 
  • graphical models, including hierarchical models

Skills

  • can apply the relevant methodologies to one or more datasets by using appropriate software implementations, and interpret the output and modify the model parameters accordingly
  • are able to 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

Competences

  • can acquire supplementary knowledge about the relevant methodologies 
  • can combine appropriate topics from the course to analyse a specific dataset.
  • can in writing describe the methodologies, results and outcome from an analysis of a specific dataset

Type of instruction

Lectures with exercises.

Extent and expected workload

This is a 5 ECTS course module and the work load is expected to be 150 hours for the student.
 

Exam

Exams

Name of examEmner inden for statistisk videnskab I
Type of exam
Written or oral exam
ECTS5
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentAs stated in the Joint Programme Regulations.

http:/​/​www.engineering.aau.dk/​uddannelse/​Studieadministration/​

Facts about the module

Danish titleEmner inden for statistisk videnskab I
Module codeF-MAT-K1-5
Module typeCourse
Duration1 semester
SemesterAutumn
ECTS5
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module

Organisation

Study BoardStudy Board of Mathematics, Physics and Nanotechnology
FacultyFaculty of Engineering and Science