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The solution is motivated by the need to model dependencies between existing experimental confirmatory HTS assays and improve prediction performance.
If inhomogeneous Poisson processes can model nonstationary data, they are not appropriate to model dependencies between points.
We show that under a common-cause failure model, dependencies between failures can affect the optimal design.
These approaches model the dependency between graphemes and phonemes and the dependency between phonemes, but do not model dependencies between graphemes [12, 17, 28].
BNs are a class of probabilistic models originating from the Bayesian statistics and decision theory combined with graph theory [24, 25], which are able to model dependencies between variables.
Notably, Arboretum does not model dependencies between different time points, modeled each time point with a separate mean and variance.
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This article can thus be considered as an extension of [3], modeling dependencies between-sources through generalizing the prior to multivariate GMM.
In DRABAL, we incorporate structure learning of a Bayesian network (BN) as a step to model dependency between the HTS assays.
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.
This paper present a technique to model dependency between variables in view of the construction of bivariate distributions based on that dependency structure.
The authors model dependency between multiple events of a patient by frailty: doctor visits during the subject's time in study.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com