|
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 |
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 can be downloaded from CRAN
ETH students can download Matlab with a free network license from Stud-IDES
Matlab tutorials, etc.
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