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. Please note that changes may occur.
Semester tution start
Number of semesters
Language of instruction
Faculty of Science and Technology, Department of Mathematics and Physics
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
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
MAT100 Mathematical Methods 1, MAT200 Mathematical Methods 2, STA100 Probability and Statistics 1
Form of assessment
Portfolio with 3 individual written assignments
A - F
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.
Tore Selland Kleppe
Stein Andreas Bethuelsen
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