Bayesian Networks are graphical models used for probabilistic reasoning and decision-making. They represent the relationships between variables in a system using a directed acyclic graph, with nodes representing variables and edges representing conditional dependencies between them.
Bayesian Networks are particularly useful in situations where there is uncertainty or incomplete information about a system, as they can model the probability distribution of the variables and infer the probabilities of unobserved variables. Bayesian Networks have been applied in a wide range of fields, including medicine, finance, and engineering, to model complex systems and make predictions and decisions based on uncertain or incomplete data.
In machine learning, Bayesian Networks have been used for tasks such as classification, regression, and clustering. They have also been integrated with other machine learning algorithms, such as decision trees and neural networks, to improve their accuracy and interpretability. Bayesian Networks are a powerful tool in the field of probabilistic modeling and decision-making, and their applications are rapidly growing in many domains.