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- Q7104536 subject Q7035969.
- Q7104536 subject Q8817213.
- Q7104536 abstract "In statistics and signal processing, the orthogonality principle is a necessary and sufficient condition for the optimality of a Bayesian estimator. Loosely stated, the orthogonality principle says that the error vector of the optimal estimator (in a mean square error sense) is orthogonal to any possible estimator. The orthogonality principle is most commonly stated for linear estimators, but more general formulations are possible. Since the principle is a necessary and sufficient condition for optimality, it can be used to find the minimum mean square error estimator.".
- Q7104536 wikiPageWikiLink Q11091747.
- Q7104536 wikiPageWikiLink Q12483.
- Q7104536 wikiPageWikiLink Q133871.
- Q7104536 wikiPageWikiLink Q1409400.
- Q7104536 wikiPageWikiLink Q190056.
- Q7104536 wikiPageWikiLink Q1940696.
- Q7104536 wikiPageWikiLink Q208163.
- Q7104536 wikiPageWikiLink Q214159.
- Q7104536 wikiPageWikiLink Q27670.
- Q7104536 wikiPageWikiLink Q3179949.
- Q7104536 wikiPageWikiLink Q320357.
- Q7104536 wikiPageWikiLink Q3527215.
- Q7104536 wikiPageWikiLink Q4217583.
- Q7104536 wikiPageWikiLink Q7035969.
- Q7104536 wikiPageWikiLink Q738628.
- Q7104536 wikiPageWikiLink Q842217.
- Q7104536 wikiPageWikiLink Q8817213.
- Q7104536 wikiPageWikiLink Q929302.
- Q7104536 comment "In statistics and signal processing, the orthogonality principle is a necessary and sufficient condition for the optimality of a Bayesian estimator. Loosely stated, the orthogonality principle says that the error vector of the optimal estimator (in a mean square error sense) is orthogonal to any possible estimator. The orthogonality principle is most commonly stated for linear estimators, but more general formulations are possible.".
- Q7104536 label "Orthogonality principle".