An Introduction to Measure Theoretic probability


This course covers the basic but nevertheless relevant (especially for Financial Mathematics I) topics of probability theory in a measure-theoretic approach.

Specific topics are

  • Definition and properties of measure and the Lebesgue integral.
  • The fundamentals of probability: probability space, random variables, conditional expectation, modes of convergence, convolutions and characteristic functions, central limit theorem.
  • An introduction to statistics: simple random sampling, introduction to estimation techniques.

More information is given in Moodle. Please inscribe there to the course.


Available in the library.

  • H. Bauer, Measure and Integration Theory, De Gruyter Studies in Mathematics, 2011.
  • H. Bauer, Probability Theory, De Gruyter Studies in Mathematics, 2011.
  • P. Billingsley, Probability and Measure, Wiley, 2012.
  • D. L. Cohen, Measure Theory, Birkenhäuser, 2010.
  • J. Jacod & P. Protter, Probability Essentials, 2nd edition, Springer, 2004.
  • A. Klenke, Probability Theory - A Comprehensive Course, Springer, 2006.
  • E. Kopp, J. Malczak & T. Zastawniak, Probability for Finance, Cambridge University Press, 2014.
  • R. Leadbetter, S. Cambanis, V. Pipiras, A Basic Course in Measure and Probability, Cambridge University Press, 2014.
  • A. N. Shiryaev, Probability, 2nd edition, Springer, 1995.
  • W. Rudin, Real and Complex Analysis, McGraw-Hill International Editions, 1987.
  • D. Williams, Probability with Martingales, Cambridge University Press, 1991.

Class Teacher

Farid Mohamed

Time and Venue

Teaching will take place online using the university's moodle system. Further information is available on the moodle system.


MSc. Finance elective course


Analysis I+II and Linear Algebra I