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For the estimation, occurrence data are inverted into the optimal solutions of μ(t) and K 0 t) under proper smoothness constraints with the assumption that the aseismic stress and changes in the triggering parameter are both stably estimated using an empirical Bayesian method.
Early algorithms were developed largely based on evolutionary constraints with the assumption that highly conserved non-coding regions should have functional potential [4].
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These constraints come from the assumption that the rules are reasonable approximations of c those used by a human expert.
Another extension proposed by Prevost made use of a prior constraint, reflecting the assumption that evidence from non-randomised studies, having been derived from study designs with potential weaknesses [ 4], may be more biased than evidence from randomised studies.
This constraint reflects the assumption that we have captured all or most of the relevant endmembers; however in the majority of cases this will not be true.
We start with the assumption that temporal and spatial knowledge usually captured by constraint networks can be represented and queried more effectively by using the scheme of indefinite constraint databases.
With the assumption that both proteins remain rigid bodies, our complex model satisfies constraints imposed by the mutagenesis pull-down results.
With the assumption that the secondary transmitter has non-causal knowledge of the codewords originated at the primary transmitter, achievable rates of the secondary user were characterized under the constraint that no rate degradation was created for the primary system.
Consistent with the assumption that selection pressure is higher in tRNAs belonging to the core set, we are able to observe higher constraints in tRNAs from the core set compared with the peripheral set for all species pairs.
In brief, an UIR was assigned in full to each flanking gene, with the assumption that disruption of gene order or changes in UIR size across species would often lead to exclusion of genes without constraint on UIR size.
The network constraint stems from the assumption that SNPs influencing the same phenotype are biologically linked.
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