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Let E be a linear expectation.
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(mathcal{E} ) is a minimal member of (S^{mathrm{sl}}); (mathcal{E} ) is a linear expectation.
(mathcal{E}_{0} ) is a maximal member of S; (mathcal{E}_{0} ) is a linear expectation.
(mathcal{E}_{0} ) is a minimal member of S; (mathcal{E}_{0} ) is a linear expectation.
Then the following statements are equivalent: (i) (mathcal{E} ) is a minimal member of (S^{mathrm{sl}}); (ii) (mathcal{E} ) is a linear expectation. .
Then the following two statements are equivalent: (i) (mathcal{E}_{0} ) is a maximal member of S; (ii) (mathcal{E}_{0} ) is a linear expectation. .
Then (mathcal{E}_{0}) is a minimal member of S if and only if (mathcal{E}_{0}) is a linear expectation.
We prove that (mathcal{E}) is a minimal member of S if and only if (mathcal{E}) is a linear expectation.
Then the following two statements are equivalent: (i) (mathcal{E}_{0} ) is a minimal member of S; (ii) (mathcal{E}_{0} ) is a linear expectation. .
We show that (mathcal{E}) is a minimal member of S if and only if (mathcal{E}) is a linear expectation with the same constraints as above, respectively.
We prove that, for a subset S of all convex expectations containing all linear expectations, (mathcal{E}) is a minimal member of S if and only if (mathcal{E}) is a linear expectation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com