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A Bernstein-von Mises theorem says that a suitably centered and scaled posterior distribution converges to the normal distribution prescribed by the local likelihood. For a fixed linear functional in a Gaussian sequence model and a prior with locally flat positive density, the limiting posterior variance is the squared norm of the functional's coefficient vector.