Many of the best known algorithms for detection/estimation problems may be viewed as message passing algorithms in a suitable graphical model, and much knowledge about such algorithms amounts to "local" message computation rules. While this approach is not new anymore, we have been continuing to develop it and to apply it to a variety of practical problems. The talk focusses on recent refinements in Gaussian messages in (locally) linear models and on expectation maximization as a message passing technique.