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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 2021-2022

Course code




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Semester tution start


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Learning outcome

After taking 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
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.
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
Portfolio with 3 individual written assignments 1/1 A - F All

The first assignment counts 10%, the second assignment count 30% and the third assignment counts 60%. Course teacher sets final marks.Candidates who fail on the assignments will not be able to submit new project assignments until the next time the course is taught.

Course teacher(s)
Course coordinator: Tore Selland Kleppe
Head of Department: Bjørn Henrik Auestad
Method of work
Four hours of problem solving/data lab per week. Lectures on online videos
Open for
Mathematics and Physics - Bachelor's Degree Programme Admission to Single Courses at the Faculty of Science and Technology City and Regional Planning - Master of Science Computer Science - Master's Degree Programme Environmental Engineering - Master of Science Degree Programme Industrial economics - Master's Degree Programme Robot Technology and Signal Processing - Master's Degree Programme Engineering Structures and Materials - Master's Degree Programme Mathematics and Physics - Master of Science Degree Programme Mathematics and Physics, 5-year integrated Master's Programme Offshore Field Development Technology - Master's Degree Programme Industrial Asset Management - Master's Degree Programme Marine- and Offshore Technology - Master's Degree Programme Offshore Technology - Master's Degree Programme Petroleum Geosciences Engineering - Master of Science Degree Programme Petroleum Engineering - Master of Science Degree Programme Technical Societal Safety - Master's Degree Programme Risk Management - Master's Degree Programme (Master i teknologi/siviling.) Exchange programme at Faculty of Science and Technology
Course assessment
Usually by forms and/or discussion according to university regulations.
Overlapping courses
Course Reduction (SP)
Statistic modelling and simulation (TE6039) 5
Statistical modelling and simulation (MET260) 10
The syllabus can be found in Leganto