Graphical models are composed of two parts: a set V of random variables describing the quantities of interest, and a graph G = (V, E) in which each vertex ν ∈ V is called node, and each edge e ∈ E, also called arc or link, is used to express the dependence structure of the data, i.e., the set of dependence relationships among the variables in V [ 22].
A model to express the dependence of the crystal melt interfacial energy on the temperature for metals is proposed.
Furthermore, the tropospheric delay gradient model (e.g., MacMillan 1995) expresses the tropospheric delay as a combination of the ZTD and an additional term to express azimuthal dependence, represented by the tropospheric delay gradient.
The edges e ∈ E are used to express the dependence relationships among the variables in X.
The most commonly used parametric model to express spatial dependence is the conditional autoregressive model (CAR).
The approach can be generalized to express the dependence of the dispersions on the expected value or on other covariates such as guanine-cytosine (GC) content or Gene Ontology annotations.
This gives rise to a measurement matrix ℳ of dimension nd × ns, which obviously depends on the distribution c of the fluorophore, and we write ℳ(c) to express this dependence.
From the viewpoint of dependency complexity, NB expresses zero-dependence because no dependency relationship exists between attributes.
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