Statistical Modeling and Simulation (STA510)

This course provides a foundation for problem solving in technology, science and economy using statistical modeling, simulation and analysis.

Course description for study year 2023-2024. Please note that changes may occur.


Course code




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Semester tution start


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The course focuses on methods to model and analyze a variety of random phenomena. The analysis will in practice often be done by simulation, but also the theoretical analysis is important. Students shall be able to implement statistical models on a computer, generate, interpret and present results. Topics that are appropriate to address: the general statistical model building, assessing the goodness of the model, estimation of distribution and parameters of the model and assess the uncertainty of estimates, bootstrap, number generators, variance reduction techniques, modeling and simulation of dependencies, modeling and simulation of stochastic processes, basic Bayesian statistics and Markov chain Monte Carlo. The course will have several exercises with the use of computers and the program R.

Learning outcome

After completing this course the student will:


  • be able to make and use statistical models for a number of problems in technology, natural science and economics
  • have knowledge of the strengths and limitations of some key techniques for statistical modeling og simulation


  • be able to implement the models (in R)
  • carry out simulations of statistical models, analyze the results statistically, and
  • be able to make assessments of uncertainty in the results

General competence

  • be able to solve complicated problems using programming and computers
  • present results in a proper manner

Required prerequisite knowledge


Recommended prerequisites

MAT100 Mathematical Methods 1, MAT200 Mathematical Methods 2, STA100 Probability and Statistics 1


Form of assessment Weight Duration Marks Aid
Written exam 1/1 4 Hours Letter grades Standard calculator

Coursework requirements

Compulsory assignments
Three compulsory assignments must be approved in order to have access to the exam

Course teacher(s)

Course coordinator:

Tore Selland Kleppe

Course teacher:

Jörn Schulz

Head of Department:

Bjørn Henrik Auestad

Method of work

Four hours of problem solving/data lab per week. Lectures on online videos

Overlapping courses

Course Reduction (SP)
Statistic modelling and simulation (TE6039_1) 5
Statistical modelling and simulation (MET260_1) 10

Open for

City and Regional Planning - Master of Science Computer Science - Master of Science Degree Programme Environmental Engineering - Master of Science Degree Programme Industrial Economics - Master of Science Degree Programme Structural and Mechanical Engineering - Master of Science Degree Programme Mathematics and Physics - Master of Science Degree Programme Mathematics and Physics - Five Year Integrated Master's Degree Programme Offshore Field Development Technology - Master of Science Degree Programme Industrial Asset Management - Master of Science Degree Programme Marine and Offshore Technology - Master of Science Degree Programme Petroleum Geosciences Engineering - Master of Science Degree Programme Petroleum Engineering - Master of Science Degree Programme
Exchange programme at Faculty of Science and Technology

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


The syllabus can be found in Leganto