This article can thus be considered as an extension of [3], modeling dependencies between-sources through generalizing the prior to multivariate GMM.
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.
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.
When modeling dependencies between datasets, CCA searches the variation shared between the datasets and separates it from the dataset-specific variation.
These predicted groups of elements were clustered with the Mixture of Hidden Markov Models (MHMM), an unsupervised clustering method capable of modeling dependencies between neighboring positions in active motif cores [see Additional File 1 Section 3].
This paper present a technique to model dependency between variables in view of the construction of bivariate distributions based on that dependency structure.
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.
