Abstract
We present procedures based on Bayesian statistics for effective field theory
(EFT) parameter estimation from data. The extraction of low-energy constants
(LECs) is guided by theoretical expectations that supplement such information
in a quantifiable way through the specification of Bayesian priors. A prior for
natural-sized LECs reduces the possibility of overfitting, and leads to a
consistent accounting of different sources of uncertainty. A set of diagnostic
tools are developed that analyze the fit and ensure that the priors do not bias
the EFT parameter estimation. The procedures are illustrated using
representative model problems and the extraction of LECs for the nucleon mass
expansion in SU(2) chiral perturbation theory from synthetic lattice data.
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