Course - Computer Intensive Statistical Methods - TMA4300
Computer Intensive Statistical Methods
About
About the course
Course content
Classical and Markov chain methods for stochastic simulation. Hierarchical Bayesian models and inference in these. The expectation maximisation (EM) algorithm. Bootstrapping, cross-validation and non-parametric methods. Classification.
Learning outcome
1. Knowledge: The student knows computational intensive methods for doing statistical inference. This includes direct and iterative Monte Carlo simulations, as well as the expectation-maximisation algorithm and the bootstrap. The student has basic knowledge in how hierarchical Bayesian models can be used to formulate and solve complex statistical problems.
2. Skills: The student can apply computational methods, such as Monte Carlo simulations, the expectation-maximisation algorithm and the bootstrap, on simple applied problems.
3. General competence: The student is able to give an oral presentation within the subject area.
Learning methods and activities
Lectures, works (projects) and student presentations of selected topics. Mandatory activities and the content of it will be communicated at the beginning of the semester. There is a final written exam.
Compulsory assignments
- Works
- Student presentations
Further on evaluation
Retake of examination may be given as an oral examination. The re-take exam will be in August.
Students are free to choose Norwegian or English for written assessments.
Recommended previous knowledge
TMA4240/TMA4245 Statistics or equivalent. The subject requires a mature understanding of statistics and we also recommend TMA4265 Stochastic Processes and TMA4267 Linear Statistical Models.
Course materials
Will be announced at the start of the course.
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| SIF5085 | 7.5 sp |
Subject areas
- Statistics
- Technological subjects