TMA4300 - Computer Intensive Statistical Methods


Examination arrangement

Examination arrangement: Aggregate score
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Project 30/100
School exam 70/100 4 hours C

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. There are obligatory 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 lectures may be given in English. If the course is taught in English, the exam may be given only in English. 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 To
SIF5085 7.5

Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  SPRING 2024

Language of instruction: English, Norwegian

Location: Trondheim

Subject area(s)
  • Statistics
  • Technological subjects
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Mathematical Sciences


Examination arrangement: Aggregate score

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD School exam 70/100 C 2024-06-04 15:00 INSPERA
Room Building Number of candidates
SL311 Sluppenvegen 14 24
Spring ORD Project 30/100 INSPERA
Room Building Number of candidates
Summer UTS School exam 70/100 C INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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