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We also compare the effectiveness between the posterior choice rule and the prior choice rule.
The decision process is modeled as a transformation of a prior choice probability P0 into a posterior choice probability P by taking into account the utility gains (or losses) and the transformation costs arising from information processing, such that P = arg max P ~ ∑ x P ~ ( x ) U ( x ) - 1 α ∑ x P ~ ( x ) log P ~ ( x ) P 0 ( x ), (7).
In this paper, we not only give the a posteriori choice of the regularization parameter which depends only on the measurable data, but also we give some different examples to compare the effectiveness between the posterior choice rule and the priori choice rule.
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It disregards how FRPs affect one another across national boundaries and how prior choices in one socio-political context can influence posterior choices in another socio-political context (cf. Bayes' theorem).
Moreover, we can also easily find that the posterior parameter choice rule works better than the prior parameter choice rule.
Moreover, we obtain the Hölder type error estimate under prior and posterior parameter choice rules.
A Bayesian data analysis is also reported that provides explicit posterior distributions over choice probabilities.
Under the posterior regularization parameter choice rule, the convergence order of the regularization solution is obtained.
We prove the convergence estimates between the regularization solution and the exact solution under the prior and the posterior regularization parameter choice rulers.
Figure 1 shows the comparisons of the numerical effects between the exact solution and its regularization solution for the prior and posterior regularization parameter choice rules.
The covariates included in the model were age (< 60, ≥ 60 years of age), sex, diagnosis (primary OA, secondary OA), Charnley class (A and B, or C), education (low, middle, or high), cohabitation (yes, no), comorbidity, type of incision (lateral, posterior incision), and choice of implant fixation (cemented; or uncemented, hybrid, reversed hybrid).
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