Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FMSF05F valid from Autumn 2020

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  • The course gives a deeper and extended knowledge of probability theory, useful for further studies in, e.g., extreme value theory and stochastic processes with applications.
  • The course deepens and expands the basic knowledge in probability theory. Central moments in the course are transforms of distribution, conditional expectations, multidimensional normal distribution, and stochastic convergence. Further, the concept of stochastic processes is introduced by a fairly thorough treatment of the properties of the Poisson process.
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • be able to explain different concepts in stochastic convergence and how they relate to each other,
    be able to explain the concepts of characteristic and moment generating functions and how these functions can be used,
    be able to describe the multi-dimensional normal distribution and the invariance properties under, e.g., linear combinations and conditioning,
    be able to explain the definition and basic properties of the Poisson process.
Competences and Skills
  • For a passing grade the doctoral student must
  • show the ability to integrate knowledge from the different parts of the course when solving problems.
Judgement and Approach
  • For a passing grade the doctoral student must
Types of Instruction
  • Lectures
  • Exercises
Examination Formats
  • Written exam
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
  • Basic course in Mathematical Statistics
Selection Criteria
  • Gut, A.: An Intermediate Course in Probability Theory. Springer, 1995.
Further Information
Course code
  • FMSF05F
Administrative Information
  • 2020-08-26
  • Professor Thomas Johansson

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