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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 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.
The current software can model dependency between the processes using either the current underlying longitudinal value (W2 i (t)= m i (t)), the rate of growth (W2 i (t)=d m i (t)/d t) or both.
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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.
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.
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.
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