Department of Mathematics

Stochastic Filtering - Theory and Applications

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Lecturer Dr. Philipp Harms
Lecture Wednesday 10-12 ML H 34.3
First lecture: September 16th
Coordinator Lukas Gonon
Exercises Thursday 14-15 ML H 41.1

First exercise class: September 24th

Aims of the course

Filtering 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.


Basic knowledge in probability theory, stochastic processes, and statistics is required. In particular, students should be familiar with the content of Martin Schweizer's lecture notes on "Brownian Motion and Stochastic Calculus". The lecture notes can be be purchased during the assistant hour ("Präsenz").

Further useful resources are the following books:

R Links

R can be downloaded from CRAN

Matlab links

ETH students can download Matlab with a free network license from Stud-IDES

Matlab tutorials, etc.



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