Sentence examples for mixing model framework from inspiring English sources

Here we build on these previous methods by introducing two mechanistic approaches (Dirichlet mixture and CLR transform) to modeling individual variability within a hierarchical Bayesian mixing model framework.

By incorporating dietary variability into the isotope mixing model framework, we have provided the tools necessary to assess the niche width of a consumer population based on variability in diet, rather than variability in isotope signatures.

We focus on the CLR transformation because it is isometric and treats components symmetrically [20], [21] and because it is numerically tractable in the mixing model framework (in contrast, the ILR involves solving polynomial roots).

Penalized splines (P-splines) in the additive mixed-models framework were used to allow for nonparametric exposure response functions (Greven et al. 2006).

This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework.

This integrated analytic framework improves the ability of mixing models to account for dietary variability, and the ability of isotope niche width analysis to directly assess the trophic links of a population.

We fit two general linear mixed models.

To further improve the spectral quality of reconstructed images, we introduced a spectral mixing model-based image restoration framework.

We outline a framework that builds on recently published Bayesian isotopic mixing models and present a new open source R package, SIAR.

We perform a repeated measures analysis using distributed lag models [ 21- 23] under a linear mixed-effects models [ 24, 25] framework, specifying a first-order autoregressive covariance structure to model the correlation between observations from the same participant, to estimate the regression coefficients between 30-minute PM2.5 and the ST-height measures.

We used a general linear mixed multivariable model analysis with variables identified a priori through a conceptual framework based on the literature.