Learning outcome
- Be able to use various probability distributions
- Have basic knowledge of extreme value statistics.
- Know about maximum likelihood estimation and have basic knowledge about estimation and confidence intervals
- Have basic knowledge of Bayesian statistics
- Know of common models for stochastic processes.
- Be able to do basic calculations for Poisson processes and Markov processes, including simple queue models.
Contents
Required prerequisite knowledge
Recommended prerequisites
Exam
Weight | Duration | Mark | Supporting materials | |
---|---|---|---|---|
Written exam | 1/1 | 4 hours | A - F | No printed or written materials are allowed. Approved basic calculator allowed. |
Coursework requirements
Course teacher(s)
- Course coordinator
- Tore Selland Kleppe
- Course teacher
- Tore Selland Kleppe
- Head of Department
- Bjørn Henrik Auestad
Method of work
Overlapping courses
Course | Reduction (credits) |
---|---|
Mathematical statistics and stochastic processes A (MOT100_1) | 7 |
Mathematical statistics and stochastic processes B (MOT110_1) | 4 |
Mathematical statistics (MOT150_1) | 4 |
Mathematical Statistics - Petroleum (MOT320_1) | 4 |
Introduction to Statistics and Probability 2 (MET270_1) | 10 |
Stochastic modeling (TE6517_1) | 4 |
Stochastic modeling (TE6517_A) | 4 |
Open to
Course assessment
Literature
Last updated: 12.12.2019