Sentence examples for quantified estimation from inspiring English sources

The exposure-based assessment has been carried out for only these three risk factors because they were the only ones with both a method for quantified estimation of health impacts and global databases of exposure assessment available.

The results of this study demonstrate the importance of quantifying estimation error for a given sampling strategy when using the stem analysis approach.

The method uses a Dynamic Generalised Linear Model with Bayesian forecasting to account for past survey sample sizes and survey results and forecast future population decay, while quantifying estimation uncertainty.

We use synthetic applications to demonstrate the ability of inverse modeling techniques combined with mechanistic reactive transport models to image reactive mineral lenses in the subsurface and quantify estimation error using indirect, commonly measured groundwater parameters.

In order to determine whether the decrease in AFC and the absence of effect of β2-agonist treatment could modulate the severity of hydrostatic pulmonary oedema, ventilated control and knockout mice were submitted to acute intravascular volume expansion by saline infusion before pulmonary oedema was quantified by estimation of the volume of alveolar epithelial lining fluid (VELF) (Fig 7).

Before the position estimates are associated with individual targets, a pertinent performance metric quantifying estimation accuracy is the so-called Wasserstein distance (WD) that measures the distance between two finite sets [35].

Furthermore, this model enables the effect of any substrate or supporting structure on measured values to be quantified and estimations for thermal conductivity of the sample itself to then be calculated where an influence is determined.

A new concept of exponentially ultimate boundedness is provided to quantify the estimation performance.

In the first comparison, one way of quantifying channel estimation performance is by MSE matrix[9], also referred to as the error covariance.

We calculated a number of statistics from each simulation to quantify the estimation error of the BSFG model.

As a second way to quantify parameter estimation performance, we used the Hessian matrix, which is the matrix of second-order partial derivatives of a function with respect to its parameters, to construct 95% confidence intervals (CI) around the maximum-likelihood point estimates for the parameters of our models.