Bayesian parameter estimation of miss-specified models
Abstract
Fitting a simplifying model with several parameters to real data of complex objects is a highly nontrivial task, but enables the possibility to get insights into the objects physics. Here, we present a method to infer the parameters of the model, the model error as well as the statistics of the model error. This method relies on the usage of many data sets in a simultaneous analysis in order to overcome the problems caused by the degeneracy between model parameters and model error. Errors in the modeling of the measurement instrument can be absorbed in the model error allowing for applications with complex instruments.
- Publication:
-
arXiv e-prints
- Pub Date:
- December 2018
- DOI:
- 10.48550/arXiv.1812.08194
- arXiv:
- arXiv:1812.08194
- Bibcode:
- 2018arXiv181208194O
- Keywords:
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- Physics - Data Analysis;
- Statistics and Probability;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Statistics - Machine Learning