On the Convergence of the Iterative "Pseudo Likelihood" Maximization Algorithm
This paper deals with the estimation of a parameter when the received observations also depends on some "nuisance" parameters. We consider a powerful iterative inference algorithm which has recently appeared in the literature. In this paper, we refer to this algorithm as iterative "pseudo likelihood" maximization (IPLM) algorithm. We give a connection between this algorithm and the problem of Bethe free energy minimization and prove several important results concerning its fixed points and its convergence properties.
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