Abstract
The paper investigates parameter estimation for the Gibbs chain and for the partially observed Gibbs chain. A recursion technique is used for maximizing the likelihood function and for carrying out the EM algorithm when only noisy data are available. Asymptotic properties are discussed and simulation results are presented.
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