Iterated Conditional Modes (ICM) is an optimization algorithm typically used in statistical inference and computer vision, particularly within the context of Markov Random Fields (MRFs) and related models. It is a variant of the more general "Conditional Modes" approach and is primarily employed for estimating the maximum a posteriori (MAP) configuration of a set of variables, given a probabilistic model.
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