Abstract.Numerically stable algorithms are developed for filtering, likelihood evaluation, generalized least squares computation and smoothing where data are generated by a state space model. The algorithms handle diffuse initial states in a numerically safe way. Singular innovation covariance matrices, such as those which arise in series with missing values, are dealt with. The algorithms generalize stable algorithms for ordinary least‐squares computation
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