Réseau bayésien
Un réseau bayésien, également connu sous le nom de réseau de croyance or a Bayes net, is a modèle graphique that represents a set of variables and their conditional dependencies using directed acyclic graphs (DAGs). In simpler terms, it’s a way to visualize and quantify the relationships between different factors or variables, particularly in scenarios involving uncertainty.
Each node in the network represents a variable, which can be either discrete or continuous. The edges (arrows) between these nodes indicate the probabilistic dependencies; for example, an arrow from node A to node B suggests that A has a direct influence on B. This structure allows us to model complex représentant les relations de manière claire et interprétable.
One of the key features of Bayesian Networks is their ability to incorporate prior knowledge and update beliefs based on new evidence. This is achieved through Bayes’ theorem, which provides a mathematical framework for updating the probability of a hypothesis as more data becomes available. As new information is introduced, the network can adjust the probabilities associated with each variable accordingly.
Les réseaux bayésiens ont une large gamme d'applications, notamment medical diagnosis, évaluation des risques, apprentissage automatique, and decision support systems. They are particularly useful in situations where information is incomplete or uncertain, allowing for informed decision-making based on the probabilistic relationships modeled by the network.