This paper reviews estimation problems with missing, or hidden data. We formulate this problem in the context of Markov models and consider two interrelated issues, namely, the estimation of a state given measured data and model parameters, and the estimation of model parameters given the measured data alone. We also consider situations where the measured data is, itself, incomplete in some sense. We deal with various combinations of discrete and continuous states and observations.