Elimination and retained observables [fdip-0018]

Eliminating selected real field values produces both a new quadratic action and a normalization factor. The prerequisites are a positive quadratic action, its normalized Gaussian law, and the reference measure and orthogonal product volume. Inverse precision first determines covariance; averaging out the discarded variables gives retained precision equal to the Schur complement \(S\), with covariance operator \(S^{-1}\) for the specified inner product.

For integrable observables of the retained variables, retained expectation agrees with original expectation after pullback. The planned states and conditional expectations chapter asks how such averaging fits a common language of observables and states. Opposite vertices of the four-cycle make the normalization and covariance explicit. The conditional kernel distinguishes fixing variables from averaging them, while singular eliminated directions explain when the integration space itself must change.

For a formal quartic perturbation, successive Gaussian eliminations agree with direct elimination through second order in the coupling. The three-coordinate example displays the generated terms and a retained connected four-point function; stationary elimination distinguishes the tree contribution from fluctuation terms.

The retained law still refers to chosen field coordinates and a chosen split. Which changes merely relabel and transport this model, and which change its predictions?