Graphical modeling uses graphs, which present the different ways the variables in a model depend on each other, to represent and visualize the model. The model’s variables can be simply associated or be connected through causal relationships. The resulting displays rely on probability and graph theory, graph algorithms and machine learning; as such, they connect concepts from statistics and computer science.
A wide range of different types of graphical models and methods have been developed in a variety of areas including, but not limited to, medical diagnosis, image understanding, speech recognition, and natural language processing. The use of graphical models can also enable understanding of social and technical features of organizations and structures. In education, such systems may extend from the classroom [Page 747]unit to the ...
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