Bayesian Networks Explained for Beginners

Bayesian networks are a type of model used to represent uncertainties and dependencies between different events.

You can imagine it as a network of points, where each point represents a thing or an event.

These points are connected by arrows that show how one thing influences another.

Bayesian networks help calculate probabilities and make predictions, even when you don't have all the information.

For example, you can use a Bayesian network to estimate how likely it is to be sick if you have certain symptoms.