Estimation of parameters in hidden Markov models

Parameter estimation from noisy versions of realizations of Markov models is extremely difficult in all but very simple examples. The paper identifies these difficulties, reviews ways of coping with them in practice, and discusses in detail a class of methods with a Monte Carlo flavour. Their performance on simple examples suggests that they should be valuable, practically feasible procedures in the context of a range of otherwise intractable problems. An illustration is provided based on satellite data.