Bayesian networks Algorithm Concepts of Bayesian networks A Deep learning architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)  Each node is associated with a probability distribution, and the edges represent the conditional dependencies between those distributions.   Bayesian networks can be used for probabilistic reasoning and decision-making by inferring the probability of a particular event, given some evidence or observations. They can also be used for decision-making by selecting the action that maximizes some expected utility.   One of the most popular algorithms for probabilistic reasoning in Bayesian networks is called the belief propagation algorithm . This algorithm uses a message-passing approach to compute the marginal probabilities of the nodes in the network.   Let's consider an example to illustrate how Bayesian networks can be used for probabilistic reasoning and decision-making. Suppose we want to predict whether a