Bayesian Network

Uncertainty is a fact of life. However, the existence of uncertainty does not mean we can’t make any predictions about cause and effect relationships. Probability theory suggests that although we cannot be certain about a single outcome of a random event, we can predict the probability of a number of possible outcomes.¹ Probability theory is about making informed inferences in the face of uncertainty.

A Bayesian network is a probabilistic graphical model. It is used to model the unknown based on the concept of probability theory. Bayesian networks show a relationship between nodes - which represent variables - and outcomes, by determining whether variables are dependent or independent. A Bayesian network works backwards, by looking at an event and suggesting possible variables that led to it. In other words, a Bayesian network provides information about probabilities regarding causes and effects of events.

For example, if you were to observe that the grass is wet, you might ask, “What is the probability that it is wet because it is raining?” To figure out the probability, you would have to calculate how often the cause of wet grass is rain, which also means knowing how often the grass is wet for a different reason (such as the sprinkler being turned on). Since the sprinkler being turned on is also dependent on whether or not it rains, a Bayesian network would map out the various conditional variables and respective probabilities.²