Conditional Independence and Separation in Graphs
Associative conditional independence with separation in graphs. Separation in graphs is basically depended upon the graph whether it is directed or undirected.
- Directed Graph:
- G = (V, E) X, Y, and Z are three disjoint subsets of nodes.
- If there is an unblocked path between X and Y, they are d-connected.
- Undirected Graph:
- G = (V, E) where, X, Y, and Z are three disjoint subsets of nodes.
- If all paths from a node in X to a node in Y contain a node in Z, then it separates X and Y in G, written as <X | Z | Y>G.
Graphical Models in R Programming
In this article, we are going to learn about graphical models in detail in the R programming language. In this, we are going to discuss the graphical model or probabilistic graphical models are statistical models that encode multivariate probabilistic distributions in the form of a graph, its real-life applications, and types, and decomposition with undirected and directed graphs, and separation in graphs.
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