Conditional probability measures how likely an event is when another event or condition is known. Bayes’ theorem uses it to update beliefs from new evidence. section 3.5
Weather prediction
In a Bayesian network, clouds can influence rain. If clouds are present, rain might have a 30% chance; without clouds, it might have a 5% chance. The model combines these dependencies to evaluate possible weather scenarios. section 3.5
Figure 3.5 An example of a Bayesian network
Use the Bayesian network diagram to see variables as nodes and conditional dependencies as directed edges. section 3.5
