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Minimum bias priors for estimating parameters of additive terms in state-space models

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2 Author(s)
Hochwald, B. ; Dept. of Electr. Eng., Yale Univ., New Haven, CT, USA ; Nehorai, Arye

We treat the problem of estimating parameters of additive terms, sometimes called bias terms, in state-space models. We consider models that depend linearly on the state but possibly nonlinearly on the parameters, where both the state and observation are corrupted by additive noise. A prior density for the parameters is introduced that, when combined with the likelihood function to form a posterior density, minimizes the bias of the posterior mean. The result is a useful prior based on ignorance. Two examples and simulations illustrate the use of the prior

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Automatic Control, IEEE Transactions on  (Volume:40 ,  Issue: 4 )