Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

401-4926-13L  Stochastic Filtering - Theory and Applications

SemesterAutumn Semester 2015
LecturersP. Harms
Periodicitynon-recurring course
Language of instructionEnglish

Catalogue data

AbstractTheory and practice of linear and non-linear filtering with applications in statistics and finance.
ObjectiveTheory and practice of linear and non-linear filtering with applications in statistics and finance.
ContentFiltering is the task of recovering unobserved state variables from noisy observations. This course covers the theoretical foundations of filtering in various levels of generality, as well as numerics and applications in statistics and finance.

The course starts with linear (Kalman) filtering and progresses to non-linear filtering for semimartingale state and observation processes. The course also includes numerical methods like Markov chain approximations, Galerkin approximations, and particle filtering, as well as applications to financial models of, e.g., interest rates or credit risk.
LiteratureBain, A. and D.~Crisan (2009). Fundamentals of Stochastic Filtering. New York: Springer.
Lipster, R. and A.~Shiryaev (2001). Statistics of Random Processes Volumes I and II (2nd ed.). Berlin: Springer Verlag.
Prerequisites / NoticePrerequisites: probability theory, basic stochastic processes, basic statistics.

Note: The former (spring semester 2013) course title of the course unit 401-4926-13L was Filter Theory -- Theory and Applications.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersP. Harms
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationoral 20 minutes
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Main linkWebsite
Only public learning materials are listed.


401-4926-13 VStochastic Filtering - Theory and Applications2 hrs
Wed10-12ML H 34.3 »
P. Harms
401-4926-13 UStochastic Filtering - Theory and Applications1 hrs
Thu14-15ML H 41.1 »
P. Harms


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Offered in

Doctoral Department of MathematicsGraduate SchoolWInformation
Mathematics MasterSelection: Financial and Insurance MathematicsWInformation