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 2022-2023. 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

Offered by
Time table

View course schedule

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 taking 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 passed in order to take the exam
Course teacher(s)
Course coordinator: Tore Selland Kleppe
Course teacher: 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
Overlapping courses
Course Reduction (SP)
Statistic modelling and simulation (TE6039_1) 5
Statistical modelling and simulation (MET260_1) 10
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 of Science Degree Programme Environmental Engineering, Master of Science Degree Programme Industrial economics, Master of Science Degree Programme Robot Technology and Signal Processing - Master's 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's Degree Programme Marine and Offshore Technology, Master of Science 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 of Science 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.
Literature
The syllabus can be found in Leganto