Probabilistic graphic models
Probabilistic Graphic Models (PGM) are generally graphs whose nodes are random variables and in which the absence of edges between these nodes indicates their independence.
They represent a formalism with the help of which one can represent various other probabilistic models, most of which were researched before the PGM. For example: Bayesian Networks , Hidden Markov Models and Markov Random Fields . PGM therefore offer the possibility of connecting these models with one another. This makes them a good tool for designing complex systems that have to deal with uncertainty. Above all, the natural access that their graph structure allows makes them a useful modeling tool.
Applications
Probabilistic graphic models are used in many scientific areas. So z. B. in pattern classification, robot navigation and in assistance systems.
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