Statistical Modeling and Simulation (STA510)

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

Recommended prerequisites

Exam

Coursework requirements

Two compulsory assignments must be approved in order to have access to the exam.

Course teacher(s)

Course coordinator:

Course teacher:

Head of Department:

Method of work

4 +2 hours of lectures and problem solving/data lab per week, self study

Open for

Course assessment

Literature