Bayesian Networks

Abstract

For some mysterious reasons, Bayesian networks are often introduced in the literature with examples concerning weather forecast (e.g., ‘Is it likely to be sunny on Sunday given the fact that it is raining on Saturday, and that my granny’s back hurts?’), or gardening issues (e.g., ‘How plausible is it that my courtyard is wet given the fact that it is raining and that the sprinkler is on?’). This may give the reader the somewhat embarrassing idea that Bayesian networks are a sort of tool you may be wishing to buy at the “Lawn & Outdoor” level of the shopping mall at the corner. As a matter of fact, there are more intriguing, yet useful, domains: for instance, soccer games. Suppose you want (anybody wants) to make a prediction on whether your favorite soccer team will win, lose, or draw tonight International League game. You can suitably describe the outcome of the match as a random variable Y, possibly taking any one of the three different values w (win), l (lose), or d (draw), according to a certain (yet, unknown) probability distribution. As unknown as this distribution may be, some probabilistic reasoning, and your prior knowledge of the situation, may help you out predicting the final outcome.