Sports announcers are notorious for crediting luck with skill. Barry Melrose comes to mind. When he's asked what a certain team has to do to win, he'll say something like "the goalie has to play good and the defense has to play good." John McEnroe stands out for emphasizing luck. Still, though, it's not clear that they're evaluating the decision based only on the result. In activities that are less strictly mathematical than video poker, it's generally understood that results play a larger part in evaluating decisions, ex ante, if only due to the lack of a cut and dried roadmap.
My knowledge of Bayes' Theorem isn't very sophisticated. As far as I know, it's a matter of garbage in, garbage out, since the initial estimate of the probability is always a factor in the final estimate of the probability. How would Bayes' Theorem go about estimating the chance that a video poker machine is gaffed? I understand that it would use the results of play on the machine, but if it needs one's initial estimate of the probability, isn't it dependent on a factor that, it's assumed, has no value?
Bob wrote: "Whether or not you have made a good decision or a bad decision should be determined at the time you make the decision — NOT sometime down the road."
Grading (good or bad) a decision before the event is classical or a priori probability. Also grading sometime down the road or after the event is Bayesian probability. I would recommend the Bayesian approach, but hey, to each their own. Bayesian is superior when there are "unknown unknowns", and, lets keep it real here, there are always "unknown unknowns".
Bayesian probability - Wikipedia
Posted by: Tom Robertson <007kzq@gmail.com>
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