Signal inference problems with non-Gaussian posteriors can be hard to tackle. Through using the concept of Gibbs free energy these posteriors are rephrased as Gaussian posteriors for the price of computing various expectation values with respect to a Gaussian distribution. We present a way of translating these expectation values to a language of operators which is similar to that in quantum mechanics. This simplifies many calculations, for instance such as those involving log-normal priors. The operator calculus is illustrated by deriving a self-calibrating algorithm which is tested with mock data.