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Dr. Andreas Ruttor
- © Copyright??
- © Copyright??
Scientific Assistant
Office: MAR 4.019
Phone: +49 30 314-23938
E-Mail: andreas.ruttor <AT> tu-berlin.de
Consulting hour according to agreement.
| Period | Occupation |
|---|---|
| since 2007 | Post-Doc Researcher at TU Berlin, Artificial Intelligence Group |
| 2007 | Ph. D. in Physics, Universität Würzburg |
| 2003-2007 | Research Assistant at Universität Würzburg, Statistical Physics Group |
| 2003 | Master in Physics, Universität Würzburg |
Research Fields
- Stochastic dynamical systems (exact and approximate inference, model selection)
- Statistical learning theory (Gaussian processes, neural networks)
- Statistical physics of complex systems
- Applications: Systems biology, data analysis, cryptography
Publications
| Citation key | Stimberg:2011:ICT |
|---|---|
| Author | Florian Stimberg and Manfred Opper and Guido Sanguinetti and Andreas Ruttor |
| Title of Book | Advances in Neural Information Processing Systems 24 |
| Pages | 2717–2725 |
| Year | 2011 |
| Editor | J. Shawe-Taylor and R.S. Zemel and P. Bartlett and F.C.N. Pereira and K.Q. Weinberger |
| Abstract | We consider the problem of Bayesian inference for continuous-time multi-stable stochastic systems which can change both their diffusion and drift parameters at discrete times. We propose exact inference and sampling methodologies for two specific cases where the discontinuous dynamics is given by a Poisson process and a two-state Markovian switch. We test the methodology on simulated data, and apply it to two real data sets in finance and systems biology. Our experimental results show that the approach leads to valid inferences and non-trivial insights. |
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