# 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.

Facts

STA500

1

10

Autumn

1

Autumn

English

Time table

## Content

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.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).

## Learning outcome

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.

None

## Recommended prerequisites

MAT100 Mathematical Methods 1, MAT200 Mathematical Methods 2, MAT210 Real and Complex Calculus, STA100 Probability and Statistics 1

## Coursework requirements

Mandatory submissions
At least 8 out of 12 exercises must be submitted and recieve a pass in order to be eligable to take the exam.

Jörn Schulz

Jan Terje Kvaløy

## Head of Department:

Bjørn Henrik Auestad

## Method of work

4+2 hours of lectures and problem solving per week, self study.

## Open for

Mathematics and Physics - Bachelor's Degree Programme Admission to Single Courses at the Faculty of Science and Technology City and Regional Planning - Master of Science City and Regional Planning - Master of Science Degree Programme, Five Years Computer Science - Master of Science Degree Programme Environmental Engineering - Master of Science Degree Programme Industrial Economics - Master of Science Degree Programme Industrial Economics - Master of Science Degree Programme, Five Year Structural and Mechanical Engineering - Master of Science Degree Programme Structural and Mechanical Engineering - Master of Science Degree Programme. Five Years Mathematics and Physics - Master of Science Degree Programme Mathematics and Physics - Five Year Integrated Master's Degree Programme Offshore Field Development Technology - Master of Science Degree Programme Industrial Asset Management - Master of Science Degree Programme Marine and Subsea Technology, Master of Science Degree Programme, Five Years Marine and Offshore Technology - Master of Science Degree Programme Petroleum Geosciences Engineering - Master of Science Degree Programme Petroleum Engineering - Master of Science Degree Programme Petroleum Engineering - Master of Science Degree Programme, Five Years Risk Analysis - Master of Science Degree Programme Exchange programme at Faculty of Science and Technology

## Course assessment

There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

## Literature

The syllabus can be found in Leganto