Probability and Statistics 2 - Study courses

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.

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.

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.

None.

	Weight	Duration	Mark	Supporting materials
Written exam	1/1	4 hours	A - F	No printed or written materials are allowed. Approved basic calculator allowed.

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.

4 hours of problem solving per week, video-lectures

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

Questionnaire and discussion with the students.

Link to reading list

Learning outcome

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Required prerequisite knowledge

Recommended prerequisites

Exam

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Overlapping courses

Open to

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Literature