Probability and Statistics 2 (STA500)

Basic and advanced issues in probability, statistical modeling using stochastic processes, estimation and statistical inference.


Course description for study year 2024-2025. Please note that changes may occur.

Facts

Course code

STA500

Version

1

Credits (ECTS)

10

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

Offered by
Time table

View course schedule

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 method, 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.

Required prerequisite knowledge

None

Recommended prerequisites

MAT100 Mathematical Methods 1, MAT200 Mathematical Methods 2, MAT210 Real and Complex Calculus, STA100 Probability and Statistics 1
or equivalent courses.

Exam

Form of assessment Weight Duration Marks Aid
Written exam 1/1 4 Hours Letter grades Approved, basic calculator, One A4 sheet of handwritten notes,

Permitted aids on the exam are simple approved calculator and one A4-sheet with your own handwritten notes.Written exam is with pen and paper

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.

Course teacher(s)

Course coordinator:

Jörn Schulz

Course coordinator:

Jan Terje Kvaløy

Head of Department:

Bjørn Henrik Auestad

Method of work

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

Overlapping courses

Course Reduction (SP)
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 for

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 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 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 supervisor, the student union 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 subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

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