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In applied statistics, optimal estimation is a regularized matrix inverse method based on Bayes' theorem.It is used very commonly in the geosciences, particularly for atmospheric sounding.A matrix inverse problem looks like this: The essential concept is to transform the matrix, A, into a conditional probability and the variables, and into probability distributions by assuming Gaussian statistics and using empirically-determined covariance matrices.

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  • In applied statistics, optimal estimation is a regularized matrix inverse method based on Bayes' theorem.It is used very commonly in the geosciences, particularly for atmospheric sounding.A matrix inverse problem looks like this: The essential concept is to transform the matrix, A, into a conditional probability and the variables, and into probability distributions by assuming Gaussian statistics and using empirically-determined covariance matrices. (en)
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  • In applied statistics, optimal estimation is a regularized matrix inverse method based on Bayes' theorem.It is used very commonly in the geosciences, particularly for atmospheric sounding.A matrix inverse problem looks like this: The essential concept is to transform the matrix, A, into a conditional probability and the variables, and into probability distributions by assuming Gaussian statistics and using empirically-determined covariance matrices. (en)
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  • Optimal estimation (en)
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