Sentence examples for model structures i from inspiring English sources

To characterize the influence of structural sources of uncertainty, alternative model structures (i.e., functional forms) can be used to represent the exposure response relationship, providing an estimate of the uncertainty in health effects as a function of structural choices.

We distinguished between the apparent scenario structure (in contrast to the underlying model structures, i.e., reasoning in terms of interrelations) and the scenario content.

Note that small β values lead to short plateaus including only the simplest model structures, i.e., those including six processes, indicating a strong preference towards simple models.

The reference CSs were calculated for the model structures (I-TASSER, IntFOLD2, Phyre2, and RaptorX) using the SHIFTX2 program, and then the predicted CSs values were implemented to the RASPnmr analyses.

In open loop, equivalence is established for three specific cases, relating to different parametrisations of the covariance expression (i.e. finite and high order approximations) and model structure (i.e. dependent and independently parameterised plant and noise models).

To test that the basic model structure (i.e., using a Boolean genetic system) did not violate the analytical results of Crow and Kimura [9] or Bürger [10] specifically, that the rate of change of genetic variance was inversely related to the number of underlying loci—I changed the network model discussed above by defining the genetic architecture as a linear Boolean [0,1] string.

In any case, Djuric [ 44] argued that the penalty for over-parameterization should strongly depend on the model structure, i.e., the types of unknown model parameters.

The first task, referred to as structure identification and often solved by a modeling expert, is to specify the model structure, i.e., the functional form of the model ODEs.

In the particular case of process-based models, we measure the complexity of a model as the number of processes in the model structure, i.e., C(m)= processes(m).

Bayesian model averaging (BMA) (Hoeting et al. 1999; Penny et al. 2010) was used to infer on the model structure (i.e., the significant connections between the ROIs), the connectivity parameters, and their modulations across the group.

Since any pullback functor is trivially left-derivable for injective model structures, (varepsilon (i)^*) is right-derivable by adjunction.