  Calibration is nowadays one of the most important processes involved in the
extraction of valuable data from measurements. The current availability of an
optimum data cube measured from a heterogeneous set of instruments and surveys
relies on a systematic and robust approach in the corresponding measurement
analysis. In that sense, the inference of configurable instrument parameters
can considerably increase the quality of the data obtained.
  This paper proposes a solution based on Bayesian inference for the estimation
of the configurable parameters relevant to the signal to noise ratio. The
information obtained by the resolution of this problem can be handled in a very
useful way if it is considered as part of an adaptive loop for the overall
measurement strategy, in such a way that the outcome of this parametric
inference leads to an increase in the knowledge of a model comparison problem
in the context of the measurement interpretation.
  The context of this problem is the multi-wavelength measurements coming from
diverse cosmological surveys and obtained with various telescope instruments.
As a first step,, a thorough analysis of the typical noise contributions will
be performed based on the current state-of-the-art of modern telescope
instruments, a second step will then consist of identifying configurable
parameters relevant to the noise model under consideration, for a generic
context of measurement chosen. Then as a third step a Bayesian inference for
these parameters estimation will be applied, taking into account a proper
identification of the nuisance parameters and the adequate selection of a prior
probability. Finally, a corresponding set of conclusions from the results of
the implementation of the method proposed here will be derived
