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.
Facts
Course code
STA510
Version
1
Credits (ECTS)
10
Semester tution start
Autumn
Number of semesters
1
Exam semester
Autumn
Language of instruction
English
Time table
Content
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:
Knowledge
- 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
Skills
- 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
None
Recommended prerequisites
MAT100 Mathematical Methods 1, MAT200 Mathematical Methods 2, STA100 Probability and Statistics 1
Exam
| 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 KleppeCourse teacher:
Tore Selland KleppeHead of Department:
Bjørn Henrik AuestadMethod 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
Course assessment
There must be an early dialogue between the course coordinator, the student 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 course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.