course-details-portlet

TMA4300

Computer Intensive Statistical Methods

Assessments and mandatory activities may be changed until September 20th.

Credits 7.5
Level Second degree level
Course start Spring 2026
Duration 1 semester
Language of instruction English
Location Trondheim
Examination arrangement Aggregate score

About

About the course

Course content

Classical and Markov chain methods for stochastic simulation. Hierarchical Bayesian models and inference in these. The expectation maximisation (EM) algorithm. Bootstrapping, cross-validation and non-parametric methods. Classification.

Learning outcome

1. Knowledge: The student knows computational intensive methods for doing statistical inference. This includes direct and iterative Monte Carlo simulations, as well as the expectation-maximisation algorithm and the bootstrap. The student has basic knowledge in how hierarchical Bayesian models can be used to formulate and solve complex statistical problems.

2. Skills: The student can apply computational methods, such as Monte Carlo simulations, the expectation-maximisation algorithm and the bootstrap, on simple applied problems.

3. General competence: The student is able to give an oral presentation within the subject area.

Learning methods and activities

Lectures, works (projects) and student presentations of selected topics. Mandatory activities and the content of it will be communicated at the beginning of the semester. There is a final written exam.

Compulsory assignments

  • Works
  • Student presentations

Further on evaluation

Retake of examination may be given as an oral examination. The re-take exam will be in August.

Students are free to choose Norwegian or English for written assessments.

Course materials

Will be announced at the start of the course.

Credit reductions

Course code Reduction From
SIF5085 7.5 sp
This course has academic overlap with the course in the table above. If you take overlapping courses, you will receive a credit reduction in the course where you have the lowest grade. If the grades are the same, the reduction will be applied to the course completed most recently.

Subject areas

  • Statistics
  • Technological subjects

Contact information

Course coordinator

Department with academic responsibility

Department of Mathematical Sciences

Examination

Examination

Examination arrangement: Aggregate score
Grade: Letter grades

Ordinary examination - Spring 2026

School exam
Weighting 70/100 Examination aids Code C Duration 4 hours Exam system Inspera Assessment Place and room Not specified yet.
Project
Weighting 30/100 Exam system Inspera Assessment

Re-sit examination - Summer 2026

School exam
Weighting 70/100 Examination aids Code C Duration 4 hours Exam system Inspera Assessment Place and room Not specified yet.