What is the expectation maximization algorithm?

By Chuong B. Do, Serafim Batzoglou. Nature Biotechnology, 2008

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What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? (2020, background): “BN parameters can be learned strictly by converting case data into relative frequencies or by other machine-learning algorithms such as expectation maximization (Dempster et al. 1977; Do and Batzoglou 2008), which is a convergent log-likelihood function that adjusts conditional probability values i”
Understanding DCM: Ten simple rules for the clinician (2013, neutral): “The intricacies of this estimation process are beyond the scope of this report; for those with a (strong) mathematical background, see (Supplementary Table 1 & Do and Batzoglou, 2008; Friston, 2002; Friston et al., 2003).”
Leaf and wood classification framework for terrestrial LiDAR point clouds (2019, methodology): “This algorithm will randomly initiate a predefined number of GMMs, each one representing a class, and then start an iterative process that tries to find the set of parameters that maximize the log probability of the observed data (Do & Batzoglou, 2008).”
Advances in Bayesian network modelling: Integration of modelling technologies (2019, methodology): “Machine-learning algorithms used in para-meterizing the probability values in BN models, such as the expectation maximization algorithm (Do and Batzoglou, 2008), can, to some degree, account for the influence of latent variables and missing data (Lauritzen, 1995).”