= EM for a missing observation in a Gaussian AR1 process
{c}
In the <expectation-maximization algorithm> for a stationary <autoregressive process of order one>, a single missing interior value has a <normal distribution> conditional on its neighbors. The E-step uses both its conditional mean and its conditional second moment, not just mean imputation. The M-step maximizes the expected stationary complete-data <log-likelihood>, including the initial-observation density and the conditional-variance contribution to the two adjacent innovation squares.
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