Syllabus for Probability Theory
Sannolikhetslära
Syllabus
- 7.5 credits
- Course code: 2ST080
- Education cycle: Second cycle
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Main field(s) of study and in-depth level:
Statistics A1N
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
First cycle
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
Second cycle
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (G), Pass with distinction (VG)
- Established: 2007-01-24
- Established by:
- Revised: 2022-10-14
- Revised by: The Department Board
- Applies from: Autumn 2023
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Entry requirements:
120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.
- Responsible department: Department of Statistics
Learning outcomes
After completing the course, the student is expected to
- be familiar with, and be able to use, different probability concepts and symbols that are often used in statistics
- know the mathematical foundations for the probability models that form the foundation for statistical inference
- have thorough knowledge of univariate and multivariate probability distributions
- have knowledge of moments and moment-generating functions
- have knowledge of different concepts of convergence
Content
The axiomatic foundation for probability theory. Combinatorics. Multivariate random variables. Conditional probability distributions. Moments and momen-generating functions. Order statistics. The multivariate normal distribution. Different concepts of convergence. Central limit theorems.
Instruction
Instruction is given in form of lectures.
Assessment
The examination takes place through a written examination at the end of the course and through written presentations of take-home assignments.
Syllabus Revisions
- Latest syllabus (applies from Autumn 2023)
- Previous syllabus (applies from Autumn 2022)
- Previous syllabus (applies from Autumn 2014)
- Previous syllabus (applies from Autumn 2013, version 2)
- Previous syllabus (applies from Autumn 2013, version 1)
- Previous syllabus (applies from Spring 2013)
- Previous syllabus (applies from Autumn 2010)
- Previous syllabus (applies from Autumn 2007)
Reading list
Reading list
Applies from: Autumn 2023
Some titles may be available electronically through the University library.
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Gut, Allan
An intermediate course in probability
2nd ed.: Dordrecht: Springer, c2009