Feature Learning for Bayesian Inference
Auf einen Blick
- Projektleiter/in : Prof. Dr. Antonietta Mira
- Co-Projektleiter/in : Prof. Dr. Fernando Perez-Cruz
- Projektteam : Dr. Carlo Albert, Prof. Alessandro Laio, Prof. Jukka-Pekka Onnela, Dr. Simone Ulzega
- Projektstatus : laufend
- Drittmittelgeber : SNF
- Kontaktperson : Simone Ulzega
Beschreibung
The goal of this project is to use interpretable Machine Learning (ML) to find low-dimensional features in high-dimensional noisy data generated by (i) stochastic models or (ii) real systems. In both cases, the problem is to disentangle the effect of high-dimensional disturbances, such as noise or unobserved inputs, from the effects of relevant characteristics (model parameters in the first case, system properties in the latter).
Publikationen
-
Bacci, Marco; Sukys, Jonas; Reichert, Peter; Ulzega, Simone; Albert, Carlo,
2023.
A comparison of numerical approaches for statistical inference with stochastic models.
Stochastic Environmental Research and Risk Assessment.
Verfügbar unter: https://doi.org/10.1007/s00477-023-02434-z
-
Ulzega, Simone; Albert, Carlo,
2023.
In:
3rd biennial meeting of the ISBA Section on Bayesian Computation (Bayes Comp), Levi, Finnland, 15-17 March 2023.