Bayesian Networks : Theoretical Approaches and Practical Applications !
Abstract : Bayesian networks are a type of probabilistic graphical model used in artificial intelligence (AI) to represent causal relationships between variables: Structure: A directed acyclic graph (DAG) that shows how variables are dependent on each other Parameters: Conditional probability distributions for each node Nodes: Stochastic nodes that represent variables, unknown parameters, or latent variables Links: Directed edges that indicate one node directly influences another Updating: The structure, prior knowledge, and data are used to update conditional dependencies Bayesian networks are used for a variety of machine learning tasks, including: clustering, supervised classification, anomaly detection, and temporal modeling. Bayesian networks are useful because they: Provide a compact representation of a joint probability distribution Encode causal and probabilistic information Can update with new data Provide a basis for algorithms for