Sentence examples for models dependencies between from inspiring English sources

For the first phase, we learn the full structure of a Bayesian network (BN) that models dependencies between the discrete target labels of the HTS assays.

We consider (i) a standard log-fold change (LFC) analysis that does not integrate dependencies between adjacent genes on DNA; (ii) a two-state HMM that models dependencies between adjacent genes on DNA; and (iii) an extension of the two-state HMM to an HMM with scaled transition matrices (SHMM) that specifically models directly adjacent genes on DNA that are in head head orientation to each other.

This article can thus be considered as an extension of [3], modeling dependencies between-sources through generalizing the prior to multivariate GMM.

The other model parameter is θ s t ; j k, which models dependency between variable X s and variable X t when they take values j and k, respectively.

These profiles and their biological interpretation are as follows: (i) the sum of mRNA expression levels, which models cooperation, (ii) the difference between mRNA expression levels models competition, (iii) the maximum mRNA expression level models redundancy, and (iv) the minimum mRNA expression level models dependency between a pair of genes.

In the current context, we choose the "working independence" assumption for modeling dependencies between probes [ 8, 9]., to avoid making any assumptions on dependence structures.

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.