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One solution to this problem is to truncate the interval of possible values.
During the pre-design step, some parameters like operating conditions are not precisely known but we have an interval of possible values, worse we only have a partial description of the problem.
Even if there is no or less probabilistic information; the interval of possible values of probability of an event can be easily specified, such as the interval value of each element's reliability of an engineering structural system.
The conditional p-value is defined by This probability is problematic to calculate because H0 refers to an interval of possible values for β g.
We therefore delimited an interval of possible values of K (K=2 6) by comparing scores produced by different criteria [ΔK, Ln Pr(X/K)].
r = correlation coefficient ‡ interval of possible values * % of children with score = 0 † % of children with maximum score † p > 0.05 (Chi-Square Test) ‡ p > 0.05 (unpaired t- test) Backward stepwise logistic regression revealed that lower maternal age at birth led to a significantly higher probability of SB in the studied sample (Table 6).
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One can also see from Figure 7b the gray thick dashed lines at the minimum value of ΩUC (S P ) taken from Figure 7a, so the interval of possible value of ΩUC (S P ) can be clearly seen for each PA in order for the coexistence to be accepted by both networks.
In such situations, it is necessary to make a choice of better parameters that produce finite intervals of possible values of a given uncertain function at each point of the parameter space.
First desideratum: if you suspend judgement about the value of a bounded real variable $X$, then it seems that different intervals of possible values for $X$ of the same size should be treated the same by your epistemic state.
Select the (alpha ) cuts (A^{X1}_alpha, A^{X2}_alpha,, A^{Xj}_alpha, ldots, A^{Xn}_alpha ) of the possibility distributions (pi _{X_1}(x_1), pi _{X_2}(x_2), ldots, pi _{X_j}(x_j), ldots, pi _{X_n}(x_n)) of the possibilistic parameters (X_j), (j = 1, 2, ldots, n,) as intervals of possible values (lfloor underline{x}_{j,alpha }, overline{x}_{j,alpha } rfloor ) (j = 1, 2, ldots, n).
Select the (alpha ) cuts (A^{X1}_alpha, A^{X2}_alpha,, A^{Xj}_alpha, ldots, A^{Xn}_alpha ) of the possibility distributions (pi _{X_1}(x_1), pi _{X_2}(x_2), ldots, pi _{X_j}(x_j), ldots, pi _{X_n}(x_n)) of the possibilistic parameters (X_j), (j = 1, 2, ldots, n,) as intervals of possible values (lfloor underline{x}_{j,alpha }, overline{x}_{j,alpha } rfloor ) (j = 1, 2, ldots, n). 3.
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Justyna Jupowicz-Kozak
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