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.
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
be able to solve complicated problems using programming and computers
present results in a proper manner
Required prerequisite knowledge
MAT100 Mathematical Methods 1, MAT200 Mathematical Methods 2, STA100 Probability and Statistics 1
Form of assessment
Three compulsory assignments must be approved in order to have access to the exam
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.