What it means to estimate a parameter, and a map of the six workhorse estimators — LS, MVUE, MLE, LMMSE, MMSE, MAP — chosen by asking what you actually know.
The classical branch in full, with MVUE as the goal, the Cramér–Rao bound as the yardstick, maximum likelihood as the workhorse, and least squares when a signal model is all you have.
The parameter becomes a random vector with a prior. Bayes' theorem turns one data record into a posterior, and MAP, MMSE, and LMMSE are three ways of summarizing it, collapsing into a single formula when everything is Gaussian.
