pavement design using mechanistic-empirical methods is often carried out based on the some assumptions for pavement layer's properties (thickness, elastic modulus, poisson's ratio and ….), loading properties(axial weight, tire coordinates, number of repetitions and …) and also the failure mechanism for different materials. fulfillment of these assumptions for evolution of pavement performance d...

many constants and coefficient effect on the modeling results. in many cases, it is possible these constants and coefficients differ from their real values in the environment, but the modeling results adapted with real boundary conditions due to other factors adjustment during modeling. uncertainty analysis is a tool for assessing the effect of uncertainty of various factors on the modeling res...

An input model is a collection of distributions together with any associated parameters that are used as primitive inputs in a simulation model. Input model uncertainty arises when one is not completely certain what distributions and/or parameters to use. This tutorial attempts to provide a sense of why one should consider input uncertainty and what methods can be used to deal with it.

BACKGROUND Errors of calibrator-assigned values lead to errors in the testing of patient samples. The ability to estimate the uncertainties of calibrator-assigned values and other variables minimizes errors in testing processes. International Organization of Standardization guidelines provide simple equations for the estimation of calibrator uncertainty with simple value-assignment processes, b...

Multi-parameter models in systems biology are typically 'sloppy': some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously a...

We explore the uncertainty in projections of emissions, and costs of atmospheric stabilization applying the MIT Emissions Prediction and Policy Analysis (EPPA) model, a computable general equilibrium model of the global economy. Monte Carlo simulation with Latin Hypercube Sampling is applied to draw 400 samples from probability distributions for 100 parameters in the EPPA model, including labor...

A spectral formalism has been developed for the “non-intrusive” analysis of parametric uncertainty in reacting-flow systems. In comparison to conventional Monte Carlo analysis, this method quantifies the extent, dependence, and propagation of uncertainty through the model system and allows the correlation of uncertainties in specific parameters to the resulting uncertainty in detailed flame str...

Flux data are noisy, and this uncertainty is largely due to random measurement error. Knowledge of uncertainty is essential for the statistical evaluation of modeled and measured fluxes, for comparison of parameters derived by fitting models to measured fluxes and in formal data-assimilation efforts. We used the difference between simultaneous measurements from two towers located less than 1 km...