Prerequisite/Recommended prerequisite for participation in the module

The module builds on knowledge obtained by the module Statistical Inference for Linear Models from the Bachelor of Science (BSc) in Engineering (Mathematical Engineering.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• have knowledge of the general linear model with random effects
• have knowledge of maximum likelihood inference for the general linear model with random effects
• have knowledge of prediction of random effects
• have knowledge of Bayesian inference
• have knowledge of prior distributions in Bayesian inference
• have knowledge of computational aspects of Bayesian inference

Skills

• can for a specific dataset identify possible sources of random variation and formulate a relevant model with random effects
• can perform maximum likelihood- and Bayesian inference for the formulated model

Competences

• can account for methodology and practical inference for different approaches to models with random effects

• Be able to reflect on the discipline's approach to academic problems at a high level and the discipline’s relationship to other subject areas.
• Be able to involve the knowledge area in solving complex problems and thus achieve a new understanding of a given subject area.

Type of instruction

As described in §17.

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

Prerequisite for enrollment for the exam

• In order to participate in the course evaluation, students on the master level must have actively participated in course progress by way of one or several independent oral and/or written contributions.

Exams

Name of exam	Bayesian Inference and Mixed Models
Type of exam	Active participation/continuous evaluation
ECTS	5
Assessment	Passed/Not Passed
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures