Abstract
In photometry, a topic of interest is to estimate the maximum precision that can be achieved by an estimator. In this context we analyse the bounds of precision on a CCD detector array in a Bayesian setting, where we have access to a prior distribution. We use the Bayesian Cramér-Rao (BCR) lower bound to analyse the gain in photometric performance in contrast with the parametric scenario where no prior information is available (or is discarded) for the inference problem.
