STA-8001 Computer-intensive Statistics - 10 ECTS
PhD students or holders of a Norwegian master´s degree in Sceince of five years or 3 + 2 years (or equivalent) may be admitted. In your educational background you need statistical courses of at least 30 ECTS credits which includes a thorough discussion of statistical methods and principles.
PhD students from other universities must upload a document from their home institution stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee.
Holders of a Master´s degree must upload a Master´s Diploma with Diploma Supplement / English translation of the diploma. Applicants from listed countries must document proficiency in English.
Proficiency in English must be documented - list of countries
For more information on accepted English proficiency tests and scores, as well as exemptions from the English proficiency tests, please see the following document:
Proficiency in english - PhD level studies
PhD students at UiT The Arctic University of Norway register for the course through StudentWeb .
External applicants apply for admission through SøknadsWeb.
All external applicants have to attach a confirmation of their status as a PhD student from their home institution. Students who hold a Master of Science degree, but are not yet enrolled as a PhD-student have to attach a copy of their master's degree diploma. These students are also required to pay the semester fee.
More information regarding PhD courses at the Faculty of Science and Technology is found here.
The course includes stochastic simulation, bootstrapping, Bayes theory, Laplace methods, the EM algorithm and Markov chain Monte Carlo (MCMC) techniques. The course is lectured in 5 parts. After each part the students must work independently with mandatory homework exercises. These must be approved to take the final exam, and the grades will be a part of the total evaluation.
Oral presentations will be required.
The course includes stochastic simulation, bootstrapping, Bayes theory, Laplace methods, the EM algorithm and Bayesian methods like Markov cahin Monte Carlo (MCMC) and Integrated nested Laplace approximations (INLA). After each part the students must work independently with mandatory homework exercises.
The candidate shall:¿
- obtain a solid knowledge and understanding of stochastic simulation, bootstrapping, Bayes theory, Laplace methods, the EM algorithm, MCMA and INLA techniques.
- be able to apply these concepts to solve theoretical problems.¿
- be able to apply these concepts in independent homework exercises using computers.
- About the course
- Campus: Tromsø |
- ECTS: 10
- Course code: STA-8001
- Responsible unit
- Department of Mathematics and Statistics