By contrast, stronger wealth effects lead to less aggressive responses, dampening the effect of model uncertainty on interest rate dynamics.
Thus, the appropriateness of using such modeled exposures in health studies depends on the pollutant and metric of concern, acceptable level of uncertainty, population of interest, study design, and other factors.
The costs and benefits of TD, along with the uncertainty of its interest repayment, provide leverage for software managers, but also could lead to problems such as increased costs and lower quality during maintenance if it is left unattended.
The process enables the quantification of uncertainty in quantities of interest (QoIs) and the determination of model consistency using tools of statistical information theory.
In addition, the application of cost-benefit analysis and attempts to understand decision-making under conditions of uncertainty became topics of interest as extensions of formal modeling techniques (Giere 1991).
Sanders and Boivie (2004) suggest that such socio-economic sorting is important for valuation under high uncertainty and conflicts of interest.
In this paper, we propose to augment this generalized probabilistic modeling approach with a stochastic sensitivity analysis in order to quantify and gain insight into separate impacts of distinct sources of uncertainty on quantities of interest.
CT images from 20 NSCLC patients were obtained for investigating the impact of four sources of uncertainty: Two region of interest (ROI) selection conditions (breathing phase and single-slice vs. whole volume) and two imaging protocol parameters (peak tube voltage and current).
Bootstrapping is a simple, widely used, but computationally intensive method for estimating uncertainty in statistics of interest [20], [21].
Our method enables the modeller to target experimental efforts in order to selectively reduce the uncertainty of predictions of interest.
