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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.


Course description for study year 2021-2022. 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

Faculty of Science and Technology, Department of Mathematics and Physics

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.
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.
Required prerequisite knowledge
None
Recommended prerequisites
MAT100 Mathematical Methods 1, MAT200 Mathematical Methods 2, MAT210 Real and Complex Calculus, STA100 Probability and Statistics 1
Exam
Form of assessment Weight Duration Marks Aid
Written exam 1/1 4 Hours A - F No printed or written materials are allowed. Approved basic calculator allowed

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
Head of Department: Bjørn Henrik Auestad
Method of work
6 hours of problem solving per week, video-lectures, 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's Degree Programme Computer Science - Master's Degree Programme Environmental Engineering - Master of Science Degree Programme Industrial economics - Master's Degree Programme Industrial economics - Master's Degree Programme - 5 year Industrial Automation and Signal Processing - Master's Degree Programme - 5 year Robot Technology and Signal Processing - Master's Degree Programme Engineering Structures and Materials - Master's Degree Programme Mechanical and Structural Engineering and Materials Science- Master's Degree Programme - 5 years Mathematics and Physics - Master of Science Degree Programme Mathematics and Physics, 5-year integrated Master's Programme Offshore Field Development Technology - Master's Degree Programme Industrial Asset Management - Master's Degree Programme Marine- and Subsea Technology - Five-Year Master's Programme Marine- and Offshore Technology - Master's Degree Programme Offshore Technology - Master's Degree Programme Petroleum Geosciences Engineering - Master of Science Degree Programme Petroleum Engineering - Master of Science Degree Programme Petroleum Engineering - Master`s Degree programme in Petroleum Engineering, 5 years Risk Analysis Master's Programme Technical Societal Safety - Master's Degree Programme Risk Management - Master's Degree Programme (Master i teknologi/siviling.) Exchange programme at Faculty of Science and Technology
Course assessment
Questionnaire and discussion with the students.
Overlapping courses
Course Reduction (SP)
Mathematical statistics and stochastic processes A (MOT100) 7
Mathematical statistics and stochastic processes B (MOT110) 4
Mathematical statistics (MOT150) 4
Mathematical Statistics - Petroleum (MOT320) 4
Introduction to Statistics and Probability 2 (MET270) 10
Stochastic modeling (TE6517) 4
Stochastic modeling (TE6517) 4
Literature
The syllabus can be found in Leganto