Probability and Statistics 2 (STA500)
Basic issues in probability. Presentation of a number of commonly used probability distributions. Short introduction to extreme-value statistic. Estimation, in particular the maximum likelihood principle,and confidence intervals in various situations. Brief introduction to Bayesian statistics. Introduction to stochastic processes, in particular Poisson processes and Markov processes. Theory and areas for applications of the various methods will be covered. Use of software (R).
Course description for study year 2023-2024. Please note that changes may occur.
Semester tution start
Number of semesters
Language of instruction
After having completed the course, the student should:
- 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.
Required prerequisite knowledge
|Form of assessment||Weight||Duration||Marks||Aid|
|Written exam||1/1||4 Hours||Letter grades||No printed or written materials are allowed. Approved basic calculator allowed|
Course coordinator:Jörn Schulz
Head of Department:Bjørn Henrik Auestad
Method of work
|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|