Students
Tuition Fee
Start Date
2026-03-16
Medium of studying
On campus
Duration
7 weeks
Details
Program Details
Degree
Masters
Major
Applied Mathematics | Mathematical (Theoretical) Statistics | Statistics
Area of study
Mathematics and Statistics
Education type
On campus
Course Language
English
Intakes
Program start dateApplication deadline
2026-03-16-
About Program

Program Overview


Course Overview

The course SF2955, Computer Intensive Methods in Mathematical Statistics, is a 7.5-credit course that aims to provide basic knowledge, understanding, and ability to solve problems in areas of statistical inference where few and simple assumptions are made as to how data have been generated.


Information per Course Offering

The course is offered in the Autumn 2025 and Spring 2026 semesters. For the Spring 2026 semester, the course starts on March 16, 2026, and ends on June 1, 2026. The course is a 50% pace of study, and the application code is 61396. The form of study is normal daytime, and the language of instruction is English.


Target Group

The target group for this course is elective for all programs as long as it can be included in the program. The course is part of the Master's Programme in Applied and Computational Mathematics, year 1, DAVE, Mandatory, and other programs such as Industrial Engineering and Management, year 1, FMIB, and Biostatistics and Data Science, year 1, Mandatory.


Course Contents

The course provides an introduction to modern Monte Carlo simulation and its applications to mathematical statistics. The course covers topics such as:


  • Sequential Monte Carlo (SMC) methods
  • Markov chain Monte Carlo (MCMC) methods
  • Bayesian statistical methods

Intended Learning Outcomes

After completing the course, the student shall be able to:


  • formulate and apply Monte Carlo simulation techniques
  • apply Monte Carlo simulation to frequentist and Bayesian statistics
  • design and implement an SMC algorithm simulating from a given sequence of probability distributions
  • design and implement an MCMC algorithm simulating from the posterior distribution of a complex Bayesian model and analyze the output

Literature and Preparations

The specific prerequisites for the course are:


  • English B / English 6
  • Completed basic course in mathematical statistics (SF1918, SF1922 or equivalent)
  • Completed basic course in numerical analysis (SF1544, SF1545 or equivalent) The course literature can be found in the course memo or in the course room in Canvas.

Examination and Completion

The grading scale for the course is A, B, C, D, E, FX, F. The examination consists of:


  • TENA - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F
  • OVNA - Assignments, 3.0 credits, grading scale: P, F The examiner may apply another examination format when re-examining individual students.

Further Information

The course is offered by the SCI/Mathematics department, and the main field of study is Mathematics. The education cycle is the second cycle. Registered students can find further information about the implementation of the course in the course room in Canvas.


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