bayesian networks
Bayesian networks are probabilistic graphical models that represent the relationships between variables and the probability of events occurring based on those relationships. They use conditional probabilities to model how the values of one variable depend on the values of other variables in the network. Bayesian networks have proven useful in many applications, such as medical decision-making and risk assessment, by allowing users to reason about complex systems with uncertain information.
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Related Concepts (4)
Similar Concepts
- approximate bayesian computation
- artificial neural networks
- bayesian inference
- bayesian inference in causal models
- bayesian models
- bayesian reasoning
- bayesian statistics
- bayesianism
- causal graphs and networks
- deep belief networks
- inference in neural networks
- neural network inference
- neural network models
- neural networks
- stochastic networks