Graphikmodelle

Graphikmodelle, also known as graphical models, are a probabilistic framework that represents dependencies between random variables using a graph. In this framework, a graph is used to encode complex probability distributions over a large number of variables. The nodes of the graph represent the random variables, and the edges represent probabilistic dependencies between these variables. If there is no edge between two nodes, it implies that the corresponding variables are conditionally independent given some other variables in the model. There are two main types of graphikmodelle: directed and undirected. Directed graphical models, also known as Bayesian networks, use directed edges to represent causal relationships or conditional probabilities. Undirected graphical models, also known as Markov random fields, use undirected edges to represent symmetric dependencies between variables. Graphikmodelle are widely used in machine learning, artificial intelligence, computer vision, and bioinformatics for tasks such as inference, learning, and prediction. They provide a concise and interpretable way to model complex systems and have been instrumental in advancements in various fields. The structure of the graph significantly simplifies the factorization of the joint probability distribution, making computations more tractable.