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Thorne and Kishino suggested that when estimating distances using concatenation of genes, to add parameters, models and priors which consider the correlation of substitution rates amongst the genes.
For analysis in MrBayes, models and priors were identical to those used to build each gene tree and were unlinked between all partitions (genes, AFLP).
Furthermore, specifying the right models and priors in a Bayesian analysis is of uttermost importance [ 60, 61].
We applied the above considered models and priors to two MTC data sets of differing size and complexity to illustrate the performance.
We used 3 levels of priors in our Bayesian analyses: regression parameter priors on the outcome model (naïve, certain misclassification models), priors for the misclassification model (certain misclassification models), and priors on the model that estimates the proportion exposed at each level of the covariates (certain misclassification models).
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Analytical results are compared with design specification models and prior research studies.
Each approach takes advantage of different signal properties or models the separation problem in slightly different ways: sparsity constraints, spatial cues, source models, and prior information are all different approaches that have been applied in the separation task.
This reveals that the influence of role models and prior entrepreneurial experience and the perception of social support factors and government policy, such as Intellectual Property Rights (IPR) are not significantly supported by EC in ASEAN-05 economies.
This disagreement is likely partially due to different error models and prior assumptions underlying each algorithm.
To obtain optimal ascertainment, many risk assessment models and prior probability models have been developed and evaluated (de la Hoya et al, 2003; Domchek et al, 2003).
The PK models and prior distributions for fixed and random effects for ketoconazole and MDZ are based on previously published meta-analyses.
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