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The Weiss-Weinstein bound (WWB) provides a lower limit on the mean-squared error (MSE) achievable by an estimator of an unknown random parameter. In this correspondence, it is shown that some previously proposed simplified versions of the bound do not always hold for constrained parameters, i.e., parameters whose distribution has finite support. These simplifications can produce results which are...
We consider the problem of finding a lower bound on the minimum mean-squared error in a Bayesian estimation problem. The bound of Young and Westerberg, which is based on determining the optimal bias function, is extended to the case of a vector parameter. A numerical study demonstrates that the bound is both tighter and simpler to compute than alternative techniques.
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