Bayesian networks
Bayesian networks are a way to represent relationships between different variables using a graphical model. Imagine a network of nodes, where each node represents a variable (like weather, disease, or a person's mood). The connections (or edges) between them show how one variable influences another. By using probabilities, Bayesian networks allow us to update our beliefs about these variables based on new information, making them useful for decision-making, predictions, and understanding complex systems. They are widely applied in fields like medicine, finance, and artificial intelligence for reasoning under uncertainty.
Additional Insights
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Bayesian Networks are graphical models that depict relationships among variables using directed graphs. Each node represents a variable, while the edges indicate dependencies. These networks allow us to represent uncertainties and update our beliefs about a system as new information becomes available. By applying Bayes' theorem, we can calculate the likelihood of certain outcomes based on prior knowledge. Essentially, Bayesian Networks help in reasoning under uncertainty, making them valuable in fields such as medicine, finance, and artificial intelligence, where understanding complex interdependencies is crucial for informed decision-making.