Statistics A

Course content

  • Conditional distributions based on densities, including conditioning in the Gaussian distribution
  • Hierarchical/mixed-effects models (theoretical and practical aspects)
  • Bayesian analyses and computations, e.g., prior and posterior distributions, credible intervals, MCMC sampling
  • Software for mixed effects models and Bayesian computations

 

Education

MSc Programme in Statistics
MSc Programme in Mathematics-Economics

Learning outcome

Knowledge

  • Conditional densities and their relations to joint and marginal densities
  • Principles behind Bayesian statistics
  • Differences between fixed and random effects in mixed effects models
  • Methods for computations in posterior distributions


Skills: Ability to

  • do computations with conditional and marginal densities, in particular with prior and posterior densities and with the Gaussian distribution
  • carry out Bayesian estimation and inference with explicit formulas (when available) and with appropriate sampling techniques
  • carry out analyses (Bayesian and frequentistic) with mixed-effecs models and hierarchical models, using appropriate software


Competencies: Ability to

  • identify relevant mixed effects models and hierarchical models (for concrete data examples)
  • present and discuss results from analyses statistical based on mixed-effecs models and hierarchical models
  • choose between principles for statistical analysis

4 hours of lectures for 7 weeks, 4 hours of exercises per week for 8 weeks (including three multiple choice tests)

Essential prerequisites: Probability distributions with densities, linear normal models, logistic and Poisson regression, R usage (all corresponding to courses “StatMet” and “MStat” (alternatively “MatStat” from previous years) and "Discrete Models" or "Regression"). The course requires maturity at the level of MSc students in statistics; it is not an introductory statistics course.

Written
Oral
Continuous feedback during the course of the semester

Written feedback will be given on voluntary assignments.

Oral feedback will be given to students if they make presentations of exercises in class.

ECTS
7,5 ECTS
Type of assessment
Continuous assessment under invigilation
Type of assessment details
The assessment is composed of two elements. The first element consists of three individual quizzes, of which the two best count a total of 50% in the final grade. The quizzes will be of one hour each and must be taken during classes (physical attendance, under surveillance). The second element is a 3 hour individual written test which counts 50% in the final grade. It must be taken during class Friday in the exam week (physical attendance, under surveillance). All aids are allowed for quizzes as well as the written test.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
Criteria for exam assessment

The student must in a satisfactory way demonstrate that she/he has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 118
  • Theory exercises
  • 29
  • Exam Preparation
  • 25
  • Exam
  • 6
  • English
  • 206

Kursusinformation

Language
English
Course number
NMAK20003U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 2
Schedulegroup
B
Capacity
No limit
The number of seats may be reduced in the late registration period
Studyboard
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Helle Sørensen   (5-7a777e7e77527f73867a407d8740767d)
Saved on the 05-05-2022

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