Estimation of parameters in hidden Markov models

Estimation of parameters in hidden Markov models

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Article ID: iaor1995700
Country: United Kingdom
Volume: 337
Issue: 1647
Start Page Number: 407
End Page Number: 428
Publication Date: Dec 1991
Journal: Philosophical Transactions of the Royal Society of London, series A
Authors: ,
Keywords: hidden Markov
Abstract:

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.

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