Bayes’ theorem updates the probability assigned to an event or hypothesis when new evidence arrives. It relates conditional probabilities: the chance of B given A is linked to the chance of A given B and the probabilities of A and B. section 3.5
Bayesian networks represent probabilistic dependencies among variables as a directed graph. Each node is a variable, and arrows indicate conditional dependence. section 3.5
Clouds and rain
A weather model can assign a 40% chance of clouds, then estimate rain at 30% when cloudy and 5% when clear. The rain probability depends on the cloud condition. section 3.5
Figure 3.5 An example of a Bayesian network
View a diagram of a Bayesian network. section 3.5
