Bayesian parameter estimation of miss-specified models
This work addresses parameter estimation challenges in physics and related fields for researchers dealing with complex data and instruments, though it appears incremental in its approach.
The paper tackles the problem of estimating parameters in miss-specified models by presenting a method that infers parameters, model error, and its statistics, using simultaneous analysis of multiple datasets to address degeneracy issues.
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.