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Along the same lines, Föllmer and Penner (2006) investigate the dynamic convex risk measures, representation of strong time consistency as a recursivity property, and they relate it to the Bellman principle of optimality.
The report notes that "the lack of concrete data contributes to the invisibility, marginalization, and disempowerment of these communities," and prevents any moves to politicize the issue, including claims for "special measures, representation, or participation in policy making, development, employment, and equality of access to basic services".
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The following theorem deals with a geometric measure representation of continuous star-shaped distributions.
Based upon a geometric measure representation, an algorithm for simulating corresponding probabilities of rather arbitrary random events is derived.
Alternatively, the geometric measure representation in Richter (2014) may be used to establish the exact distributions of several of these statistics, or at least to derive suitable approximations.
If the star-spheres are represented in a certain analytical way, Nolan (2016) aims to exploit the geometric measure representation approximatively in a sense, not yet explicitly defined.
To measure representation of abstract (numeric) magnitude, independent of any content-specific information, a series of line estimation tasks were given.
A geometric measure representation of the standardized p-power exponential law applies to show that this distribution is the p-generalized Fisher or F n,n (p -distribution.
Numerous types of applications of special cases of the geometric measure representation in Theorem 7 are surveyed in (Richter [2009], [2012]).
To this end, one might use various analytical tools like, e.g., a geometric measure representation as a starting point of explicit analytical derivations.
The corresponding probability distribution allows the geometric measure representation Phi_{g, K, nu}(B ={frac{1}{I(g)}}intlimits_{0}^{infty} r^{n-1}g(r mathfrak{F}_{S} B ,r dr, Bin mathfrak{B}^{n} (6).
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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