Course content

Classical methods for stochastic simulation, Markov chain Monte Carlo methods. Graphical models and Bayesian inference. Kernel estimators. Bootstrapping, cross-validation and non-parametric methods. Classification.

Learning outcome

This subject gives an introduction to modern computer based techniques for statistical inference. The students will through the mandatory exercises be capable of applying the theory in simple situations.

Learning methods and activities

Lectures and compulsory computer exercises. The lectures may be given in English. Portfolio assessment is the basis for the grade awarded in the course. This portfolio comprises a written final examination 70% and exercises 30%. The results for the constituent parts are to be given in %-points, while the grade for the whole portfolio (course grade) is given by the letter grading system. Retake of examination may be given as an oral examination.

Compulsory assignments

• Exercises

Recommended previous knowledge

Subject TMA4240/TMA4245 Statistics. The subject require a mature understanding of statistics and we also recomend TMA4265 Stochastic Processes and TMA4267 Linear Statistical Models.

Course materials

Will be announced at the start of the course.

Credit reductions