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.
Web links
Further content in the sister projects of Wikipedia:
|
||
![]() |
Commons | - multimedia content |