… of conducting a prior sensitivity analysis when implementing Bayesian estimation methods.
… through a sensitivity analysis. We argue that conducting a prior sensitivity analysis is equally …

… In this study, the sensitivity analysis, screening, and optimization of 63 model parameters of
… sensitivity analysis method and the Markov chain Monte Carlo (MCMC) method of Bayesian …

… Preferably, preregistering the specific priors or even alternative priors, or running sensitivity
analysis to explore to what extent posterior results are stable irrespective of the selected prior…

… of this sensitivity analysis might seem intimidating: if Bayesian … (that specificities and
sensitivities of the tests should not be so … Performing the sensitivity analysis was fine—it helped us …

… 2.2 Summary of Approach We propose a method for model-based sensitivity analysis that
… data distributions in this approach, f t obs , are free of the sensitivity parameter ψ, which …

… In this paper, a method for uncertain system reliability and imprecise sensitivity analysis
is proposed based on a Bayesian network, a probability box and the pinching method. The …

… , sensitivity analysis (SA) has been receiving increasing attention in the recent decade. …
most significant and confusing parts when conducting a sensitivity analysis [51]. On the one hand…

… Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling.
The tremendous potential benefits of SA are, however, yet to be fully realized, both for …

… Naïve Bays classifier using different training set instead of the cross validation method using
10 folds classification. A sensitivity analysis, as a contribution to this paper, will be applied …

… in terms of statistical theory, compares ordinary sensitivity analysis to probabilistic bias
analysis, and concludes by considering when these analyses may be helpful or essential. …