Course content

The course gives a broad introduction to classical probability theory and asymptotic techniques towards applications in statistics. Together with course MA8701 General statistical methods it provides a theoretical basis for PhD students in statistics. The contents include basic probability theory, convergence of sequences of random variables, characteristic functions, classical limit theorems, asymptotic properties of statistical methods.

Learning outcome

1. Knowledge The course gives a broad introduction to classical probability theory and asymptotic techniques towards applications in statistics. Together with course MA8701 General statistical methods it provides a theoretical basis for PhD students in statistics. The contents include basic probability theory, convergence of sequences of random variables, characteristic functions, classical limit theorems, asymptotic properties of statistical methods. 2. Skills The students should learn and be able to use the basic methods of probability theory and asymptotic analysis as mentioned above. They should be able to apply these methods to various problems in probability theory and statistical inference, as well as in applied mathematics. 3. Competence The students should be able to participate in scientific discussions and conduct research in probability and in asymptotic analysis at a high international level. They should be able to participate in interdisciplinary projects involving these topics.

Learning methods and activities

Lectures, alternatively guided self-study.

The course is taught only if a sufficient number of students register. If too few students register, then the course is only given as a guided self study.

Recommended previous knowledge

The course requires good statistical background, such as TMA4295 Statistical inference or equivalent.

Course materials

Will be announced at the start of the course.