The Book of Why: The New Science of Cause and Effect

by Judea Pearl

This example vividly illustrates a number of advantages of Bayesian networks. Once the network is set up, the investigator does not need to intervene to tell it how to evaluate a new piece of data. The updating can be done very quickly. (Bayesian networks are especially good for programming on a distributed computer.) The network is integrative, which means that it reacts as a whole to any new information. That’s why even DNA from an aunt or a second cousin can help identify the victim. Bayesian networks are almost like a living organic tissue, which is no accident because this is precisely the picture I had in mind when I was struggling to make them work. I wanted Bayesian networks to operate like the neurons of a human brain; you touch one neuron, and the entire network responds by propagating the information to every other neuron in the system.