Bayesian Networks
Bayesian Networks are probabilistic graphical models that represent a set of random variables and their conditional dependencies via a directed acyclic graph. They are used to model complex systems and make predictions or decisions under uncertainty. Bayesian Networks are based on Bayes' theorem and the concept of conditional probability. They allow for efficient inference and updating of probabilities as new information becomes available. Bayesian Networks have applications in various fields such as finance, healthcare, and engineering. They are also used in machine learning for classification, regression, and clustering tasks.
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