Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Simulation of right and left truncated gamma distributions by mixtures
Statistics and Computing
Bayesian posterior mean estimates for Poisson hidden Markov models
Computational Statistics & Data Analysis
Parameter estimation in a model for misclassified Markov data - a Bayesian approach
Computational Statistics & Data Analysis
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This paper synthesizes a global approach to both Bayesian and likelihood treatments of the estimation of the parameters of a hidden Markov model in the cases of normal and Poisson distributions. The first step of this global method is to construct a non-informative prior based on a reparameterization of the model; this prior is to be considered as a penalizing and bounding factor from a likelihood point of view. The second step takes advantage of the special structure of the posterior distribution to build up a simple Gibbs algorithm. The maximum likelihood estimator is then obtained by an iterative procedure replicating the original sample until the corresponding Bayes posterior expectation stabilizes on a local maximum of the original likelihood function.