Syllabus for Probability Theory

Sannolikhetslära

  • 7.5 credits
  • Course code: 2ST080
  • Education cycle: Second cycle
  • 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: 2014-08-30
  • Revised by: The Department Board
  • Applies from: week 35, 2022
  • Entry requirements: 120 credits including 90 credits in statistics.
  • 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.

Reading list

The reading list is missing. For further information, please contact the responsible department.