After successful completion of the course, students are able to. work with set systems, measures, and abstract integrals and analyze
general probability measures and special distributions and random variables or measurable functions
Probabilty Spaces, distributions, measure theoretic foundations of probability theory, independence and conditional probability, distribution functions,random variables, Lebesgue integral and expectation, comparison of Riemann and Lebesgue integral, laws of large numbers
Billingsley, P.: Probability and Measure, Wiley, New York, 1986.
Bogachev, V.: Measure Theory, Vol. I & II, Springer, Berlin, 2007.
Elstrodt, J.: Mass- und Integrationstheorie, Springer, Berlin, 2005.
Feller, W.: An Introduction to Probability Theory and its Applications, Vol.1 and 2 Wiley, New York, 1968.
Georgii, H.O.,: Stochastik, de Gruyter, Berlin, 2002
Kusolitsch, N., : Mass- und Wahrscheinlichkeitstheorie, Springer, Wien, 2014
Rosanov, J.A.,: Stochastische Prozesse,Akademie, Berlin,1975
Williams, D.: Probability with Martingales, Cambridge University Press, Cambridge, 2010.
