From David Barber

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Recent research highlights

The auxiliary variable trick for deriving Kalman smoothers The derivation explains how to express the backward (beta) message in a form that is numerically stable.

MSC in Computational Statistics and Machine Learning

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Page last modified on January 16, 2019, at 11:29 AM