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Other input data in the BIA model (BIA data) differ from the standard health economic dataset.
This information is needed to decide on the type of BIA model (prevalence-based BIA model, incidence-based BIA model or a BIA model based on both prevalence and incidence) and its characteristics (need of health states, pathways, follow-up period) and effectiveness measure(s).
Therefore, the handling of uncertainty in cost-effectiveness modelling studies may be applicable to a BIA model.
The first part of this paper describes the modelling design of a BIA and the required data input for the BIA model and its data sources.
Language membership information can then modulate lexical processing, in line with the architecture of the BIA model, but not the BIA + model.
The second part describes the handling of uncertainty in the BIA model.
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a The bias model using default κ 0 diminution and b the bias model using κ 0 diminution with f E. Bias model B outperforms bias model A. Site-corrected simulations using the HVSR of the mainshock and weak events both fit well with the observed data.
Table 5 The planted stand models for mean top height and basal area were validated using the one-at-a-time cross-validation method MTH Basal area RMSE Bias RMSE Bias Model 1.301 0.09 7.58 0.44 Validation 1.555 0.37 8.327 0.41.
The presented research examines the PPP-AR products generated from the FCB (Fractional Cycle Bias) model and IRC (Integer Recovery Clock) model that have been transformed into the DC (Decoupled Clock) format and applied within the PPP user solution.
Fig. 8 Bias models showing the logarithm (base 10) of the ratio of the observed to the simulated Fourier spectra, averaged over 120 sites.
Sensory Bias models emphasize the role of sensory system variation in driving divergence in mate preferences [ 5– 9].
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