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LDA [21] can be depicted as a directed graphical model as shown in Figure 1a.
For "causal" analysis typically the inference of a directed graphical model is required.
The BNT is a directed graphical model based on the study of Bayes probability and network cluster in MATLAB.
In brief, a directed graphical representation of each metabolic map was created in which the nodes represented enzymes and the edges represented a shared substrate/product between two enzymes.
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Besides the widely used factor graphs, which capture detailed information about the factorization of a joint probability distribution [23, 26, 27], especially directed graphical models, such as Bayesian networks, have been shown to be well-suited for modeling causal probabilistic relationships of sequential data like speech [28, 29].
BN is also known as directed graphical models, where the links of the graphs have a particular directionality indicated by arrows.
There are two main types: directed graphical models (or Bayesian networks) and undirected graphical models (or Markov networks).
Perhaps not coincidentally, the senior author of this entry, who helped to develop the directed graphical representation into search and prediction procedures for causal hypotheses, studied with two of Reichenbach's doctoral students, Schuster and Salmon.
Glymour is also more optimistic than us about the potential of using directed graphical models (DGMs) to discover causal relations in neuroimaging research; we briefly address this issue toward the end of our rejoinder.
We use the directed graphical model (Bayesian network) shown in Figure 1.
The probabilistic model is shown using standard directed graphical notation in Figure 1.
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