Furthermore, using a discrete mixtures modelling approach we explain the heterogeneous nature of preferences for the module attributes and the students' lexicographic preference orderings.
Nevertheless, our analysis provides evidence of how a network modeling approach can increase the variance explained and, moreover, provide interpretable models of genetic interaction.
This chapter explains the integrate-and-fire modelling approach and the effect of stochastic events on model dynamics.
We start by describing the general model approach and explaining the methods used to estimate fishing-induced selection differentials.
While each of these statements may seem innocuous, they reflect a student-deficit model approach to explaining challenges in teaching and learning, in which the underlying assumption is that the root of the problem lies with students.
Such detailed modeling approaches have succesfully explained results from synthetic biology experiments.
The number of differentially expressed genes for the Genotype × Environment interaction term was substantially reduced when all of the first-order interaction terms were fitted to the model (approach 3); thus, it explained less variation in the data.
There are alternative theoretical approaches explaining modulations of the ERN: the error detection approach [33] and its extension, the reinforcement learning model [47].
Informed by a realist approach, the model explained variation in (a) policy implementation (b) the breastfeeding outcomes, whereas the quantity of intervention delivered did not.
The general linear model approach is used to explain the variation attributable to different experimental factors and their interactions.
