Anzeige
Erschienen in:
2009 | OriginalPaper | Buchkapitel
Undirected Graphical Models
verfasst von : Trevor Hastie, Robert Tibshirani, Jerome Friedman
Erschienen in: The Elements of Statistical Learning
Verlag: Springer New York
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
A graph consists of a set of vertices (nodes), along with a set of edges joining some pairs of the vertices. In graphical models, each vertex represents a random variable, and the graph gives a visual way of understanding the joint distribution of the entire set of random variables. They can be useful for either unsupervised or supervised learning. In an
undirected graph
, the edges have no directional arrows. We restrict our discussion to undirected graphical models, also known as
Markov random fields
or
Markov networks
. In these graphs, the absence of an edge between two vertices has a special meaning: the corresponding random variables are conditionally independent, given the other variables.