Sentence examples for multiple model parameters from inspiring English sources

In this structure, each context will be assigned to several terminal leaves with certain membership functions, and consequently, each training sample affects multiple model parameters, and generalization should be improved.

Three parameter estimation problems are used to illustrate the use of the proposed two-step parameter estimation procedure and to show that the minimization of parameter correlations and relative errors are indeed possible through proper manipulation of reference temperatures in problems involving multiple model parameters.

Probabilistic sensitivity analysis was performed to assess the decision uncertainties in multiple model parameters.

Obesity (body mass index [BMI] ≥ 30 kg/m) affects outcomes based on its age- and cohort-specific prevalence 20– 23 through its impact on multiple model parameters.

Second, we performed a probabilistic sensitivity analysis using 10,000 Monte Carlo simulations to evaluate the combined impact of multiple model parameters on the estimated cost effectiveness of the new oral anticoagulants.

The GSA technique allows exploration of the sensitivity of specific model outputs to simultaneous variation of multiple model parameters within broad parameter space, and has recently proven its value for the analysis of the range of biological systems [ 45- 47].

This section describes the concepts of the proposed analysis of neutral solution spaces, i.e., of multiple model parameter sets with similar dynamics; all technical details are presented in the later Methods section.

Secondly, multiple model parameter sets (k1,n, k2,n, k3,n, k4,n) to produce a "good fit" were selected if their associated score was both strongly and positively correlated with MMSE score, as well as being strongly and negatively correlated with IADL score using a nonparametric Spearman correlation coefficient as the criterion distance of good fit.

The elasticity index (EI) is a measure of the relative change in selected variable to the relative change in the CLint,T, normalizing the scale for comparison of the sensitivity of multiple model output parameters.

Multiple logistic model parameters are summarised in Table 3. Positive B coefficients in the logistic regression model indicate that higher SVI and sBP/LVESVI are independently indicative of increased probability of survival.

Table 1 Statistical parameters of multiple regression for model Parameters Value R 2 0.8632 Adjusted R 2 0.8595 Coefficient of variation 0.2770 Mean square error 1.014345 Square root of MSE 1.007147.