Parameter Inference for Stochastic and Deterministic Dynamic Biological Processes
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of WS 2011/2
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
|available module versions|
|WS 2014/5||SS 2012||WS 2011/2|
MA5603 is a semester module in English language at Master’s level which is offered every semester.
This module description is valid from WS 2011/2 to WS 2012/3.
|Total workload||Contact hours||Credits (ECTS)|
|90 h||30 h||3 CP|
Content, Learning Outcome and Preconditions
a. Ordinary differential equation
b. Partial differential equation
c. Markov jump processes
d. Chemical Master Equation
2. Probability theory
3. Nonlinear optimization
4. Parameter inference
b. Maximum likelihood
5. Uncertainty analysis
a. Asymptotic methods
c. Profile likelihood
d. Bayesian analysis
The participants will develop several MATLAB programs for parameter estimation.
The participants will work in groups and present recent advances in the field. This will improve team working and presentation skills.
2) The participants can independently solve common parameter estimation problems for in dynamical systems.
3) The participants can analyze the uncertainty of parameter estimates using different methods.
4) The participants can present own results and critically evaluate them.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Parameter Inference for Stochastic and Deterministic Dynamic Biological Processes||Hasenauer, J. Theis, F.|
Learning and Teaching Methods
Description of exams and course work
The exam may be repeated at the end of the semester.