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Calibration is nowadays one of the most important processes involved in theextraction of valuable data from measurements. The current availability of anoptimum data cube measured from a heterogeneous set of instruments and surveysrelies on a systematic and robust approach in the corresponding measurementanalysis. In that sense, the inference of configurable instrument parameterscan considerably increase the quality of the data obtained.  This paper proposes a solution based on Bayesian inference for the estimationof the configurable parameters relevant to the signal to noise ratio. Theinformation obtained by the resolution of this problem can be handled in a veryuseful way if it is considered as part of an adaptive loop for the overallmeasurement strategy, in such a way that the outcome of this parametricinference leads to an increase in the knowledge of a model comparison problemin the context of the measurement interpretation.  The context of this problem is the multi-wavelength measurements coming fromdiverse cosmological surveys and obtained with various telescope instruments.As a first step,, a thorough analysis of the typical noise contributions willbe performed based on the current state-of-the-art of modern telescopeinstruments, a second step will then consist of identifying configurableparameters relevant to the noise model under consideration, for a genericcontext of measurement chosen. Then as a third step a Bayesian inference forthese parameters estimation will be applied, taking into account a properidentification of the nuisance parameters and the adequate selection of a priorprobability. Finally, a corresponding set of conclusions from the results ofthe implementation of the method proposed here will be derived

A Bayesian approach to the inference of parametric configuration of the signal-to-noise ratio in an adaptive refinement of the measurements