Stochastic Approximation Expectation Maximization

Identity Notes & Concept Scope The Delyon-Lavielle-Moulines SAEM construction fits a fixed observed data set while stochastic approximation controls integration noise. It is distinct from fresh-batch MCEM, stochastic-gradient parameter steps, parameter-iterate averaging, and online learning from newly arriving observations. A persistent MCMC simulation step is a separately scoped extension because exact conditional draws and Markovian perturbations require different arguments. All 442 pinned AIO labels/scoped synonyms and 470 preceding native IDs, names and aliases were compared with no normalized identity match. Ignored-inclusive whole-checkout fixed-string searches found 218 matched lines in each checkout; context windows for all 41 distinct matched texts were read. BayesianDataAugmentation, HiddenMarkovTree and IterativeConditionalEstimation mention applications or adjacent estimators, not these identities. All 225 semantic label/synonym rows were recomputed and verified identical to the preceding fully read inventory. Sixteen annotated AIO nodes and outgoing edges comprise fourteen verified prior readings and two new readings. Thirty-four contextual YAMLs comprise twenty-seven byte-verified prior readings and seven full new readings. Absence is bounded to this pin, maintained corpus and inspected neighborhood. No normalized name/alias matches among 442 pinned and 470 maintained records. BayesianDataAugmentation, HiddenMarkovTree and IterativeConditionalEstimation matches are applications or adjacent estimators, not duplicate generic MCEM, SAEM or MCMC-SAEM records. The inspected sufficient-statistic maximization and fixed-data scope distinguish SAEM from averaged parameter gradients, ICE and sequential-task continual learning. Persistent Markovian simulation distinguishes the MCMC extension from the exact-conditional-draw base theorem.