Course content

This subject is normally taught every second year, next
time spring 2018. A condition is that sufficiently many students are registered.
The course will give a theoretical and methodological
introduction and discussion of computational intensive statistical methods, but assumes also good computational skills. Topics to be discussed are a selection of the following; theory and methods for Markov chain Monte Carlo, Hidden Markov chains, Gaussian Markov random fields, mixtures, non-parametric methods and regression, splines, bootstrapping, classification and graphical models, latent Gaussian models and their approximate Bayesian inference. Relative weighting of the various topics will vary according to need.

Learning outcome

1. Knowledge.
The course will give a theoretical and methodological introduction and discussion of computational intensive statistical methods, but assumes also good computational skills. Topics to be discussed are a selection of the following; theory and methods for Markov chain Monte Carlo, Hidden Markov chains, Gaussian Markov random fields, mixtures, non-parametric methods and regression, splines, bootstrapping, classification and graphical models, latent Gaussian models and their approximate Bayesian inference.

2. Skills.
The students should learn and be able to use the basic computational intensive techniques in the modern theoretical statistics. In particular, Markov chain, Monte Carlo, Hidden Markov chains, Gaussian Markov random fields, mixtures, non-parametric methods and regression, splines, bootstrapping, classification and graphical models, latent Gaussian models and their approximate Bayesian inference.


3. Competence.
The students should be able to participate in scientific discussions and conduct researches in statistics on high international level. They should be able to participate in applied projects involving statistical methods and apply their knowledge in problems in theoretical statistics.

Learning methods and activities

Lectures, alternatively guided self-study.

Compulsory assignments

• Exercises

Recommended previous knowledge

TMA4300 Computer Intensive Statistical Methods, TMA4295 Statistical Inference, TMA4267 Linear statistical models.

Course materials

Will be announced at the start of the course.