Bayesian networks are probabilistic graphical models that represent variables and their dependencies as a directed acyclic graph. They can be used to predict the likelihood of an event occurring given certain known causes, and can also be used to compute the probabilities of various diseases given symptoms. They can also be extended to dynamic Bayesian networks and influence diagrams for decision problems under uncertainty.
Stanford University
Winter 2023
An in-depth study of probabilistic graphical models, combining graph and probability theory. Equips students with the skills to design, implement, and apply these models to solve real-world problems. Discusses Bayesian networks, exact and approximate inference methods, etc.
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