We illustrate these methods using multivariate t-models with known or unknown degrees of freedom and Poisson models for image reconstruction. Second, summarizing various recent extensions of the EM algorithm, we formulate a general alternating expectation-conditional maximization algorithm AECM that couples flexible data augmentation schemes with model reduction schemes to achieve efficient computations. First we introduce the idea of a `working parameter' to facilitate the search for efficient data augmentation schemes and thus fast EM implementations. automatic monotone convergence in likelihood). ![]() ![]() Celebrating the 20th anniversary of the presentation of the paper by Dempster, Laird and Rubin which popularized the EM algorithm, we investigate, after a brief historical account, strategies that aim to make the EM algorithm converge faster while maintaining its simplicity and stability (e.g.
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