philosophers have tried to define causation in terms of probability, using the notion of “probability raising”: X causes Y if X raises the probability of Y.

“causation” is simple, if a little metaphorical: a variable X is a cause of Y if Y “listens” to X and determines its value in response to what it hears.

As new evidence is entered into the network—at any place in the network—the degrees of belief at every node, up and down the network, will change in a cascading fashion.

A Bayesian statistician, on the other hand, would say, “Wait a minute. We also need to take into account our prior knowledge about the coin.” Did it come from the neighborhood grocery or a shady gambler?

we have to teach the computer how to selectively break the rules of logic.

the vertical line means “given that you see.”

“collider.”

a large part of human belief about future events rests on the frequency with which they or similar events have occurred in the past.

after the inference engine has received the Data input, it will use the recipe to produce an actual Estimate for the answer, along with statistical estimates of the amount of uncertainty in that estimate.