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We have over a million daily visitors, do not miss out on any oportunit the medium concentration with inclusions were too large to be realistically implemented on a computer, we instead generated the Monte Carlo realisations starting from a point that had already been sampled from the posterior. The $10^6$ per realisation were sampled using the same `JAGS` model as the original posterior of 10000 samples.\n\nTo compare the method against the commonly used PERT-based approach of assessing mixtures, we use a credible interval based on the 85th-15th percentile to evaluate the concentration range of a given inclusion. A PERT prediction on the NA-14 dataset is shown in \\[fig:credible\\_intervals\\]. Although the standard PERT estimator is conservative for smaller intervals, it provides an accurate representation of the true concentration of the mixture. In contrast, the Bayesian approach accurately predicts the overall concentration for the range of each mixture, indicating that the Bayesian approach is more appropriate to perform estimates of the concentration range.\n\n![Credible intervals for mixtures by the PERT estimator using the NA-14 dataset for PERT distributions; compare to the Bayesian credible interval in \\[fig:simu\\_model\\_b\\]. The shaded region is the 95% credible interval, and the dot-dash line is the true value.[]{data-label=\"fig:credible_intervals\"}](figures/fig_NA14_credible_intervals)\n\nRelating Mixability to Performance in NLP\n-----------------------------------------\n\nFollowing the previous work\n7582aa13b2\nplease help me thanks.. 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