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This model dependency must be considered when interpreting and comparing results from different research groups.
This means that initial model dependency is not so strong as long as we give sufficient space for variables.
Here we present DRABAL, a novel MLC solution that incorporates structure learning of a Bayesian network as a step to model dependency between the HTS assays.
In DRABAL, we incorporate structure learning of a Bayesian network (BN) as a step to model dependency between the HTS assays.
This detector is an extension of the binary integration detector, while overcoming its shortcomings of the statistical model dependency and maintaining its high efficiency.
A nonlocal constructive design approach leads to a passivated control scheme with linearity, decentralization and reduced model dependency features as well as nonlocal closed-loop behavior.
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These approaches model the dependency between graphemes and phonemes and the dependency between phonemes, but do not model dependencies between graphemes [12, 17, 28].
If inhomogeneous Poisson processes can model nonstationary data, they are not appropriate to model dependencies between points.
The solution is motivated by the need to model dependencies between existing experimental confirmatory HTS assays and improve prediction performance.
We show that under a common-cause failure model, dependencies between failures can affect the optimal design.
Traditional Probabilistic Risk Assessments (PRAs) model dependencies through deterministic relationships in fault trees and event trees and parametric models of common cause failure (CCF) events.
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