Let's take a recent election as an example: A Bayesian pollster began with a certain set of prior probabilities. That the college educated were more likely to

Let's take a recent election as an example: A Bayesian pollster began with a certain set of prior probabilities. That the college educated were more likely to vote in previous elections, for example, informed the sample population, because it wouldn't make much sense to ask the opinions of those who would stay home. Thus, based on priors that were updated with new empirical data, a new set of probabilities emerged, that gave a certain candidate a high probability of victory. Members of the voting public, aware of this high probability, decided that this meant with certainty that this candidate would win and therefore decided to stay home on election day. In reality the Bayesian models were incorrect as amongst other factors, a much higher number of non-college educated individuals decided to vote and to vote for the other candidate. As it is with Bayesian intelligence, shared as much by pollsters as machine learning algorithms: Real-time heads up display Keeps the danger away But only for the things that already ruined your day.