### Learning outcome

*After having completed the course one 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.

### 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