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Regularized (stabilized) versions of exponential and linear forgetting in parameter tracking are shown to be dual to each other. Both are derived by solving basically the same Bayesian decision problem where Kullback-Leibler divergence is used to measure (quasi) distance between probability distributions of estimated parameters. The type of forgetting depends solely on the order of arguments in...
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