Could the Reverend Bayes have helped evaluate a suspect horse race tipster?

punter

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Hi

Below is a sample of a type of problem I'm struggling with. I’d be enormously grateful for a step-by-step worked solution to it in simple terms. I’m embarrassed to admit that I’m Bayesianly challenged.:(

During a free trial period, an Internet horse race tipster has given out 100 tips as to the winner in each of the same number of races. 25% of these won, and the associated starting odds were high enough to enable his followers to turn a profit. Based on his success, he now decides to charge for the service. During his next 60 predictions his success rate drops to 10% winners, and his customers now lose. At this point how do they calculate the a posteriori probability of this decrease in successful predictions? They would want to do this in order have a quantitative basis (presumably a significance level) for deciding whether to continue with the service.

An aside: I’ve read that the good Reverend developed his theorem to calculate horserace probabilities.

Many thanks in advance,

Jim
 
Last edited:
Hi

Below is a sample of a type of problem I'm struggling with. I’d be enormously grateful for a step-by-step worked solution to it in simple terms. I’m embarrassed to admit that I’m Bayesianly challenged.:(

During a free trial period, an Internet horse race tipster has given out 100 tips as to the winner in each of the same number of races. 25% of these won, and the associated starting odds were high enough to enable his followers to turn a profit. Based on his success, he now decides to charge for the service. During his next 60 predictions his success rate drops to 10% winners, and his customers now lose. At this point how do they calculate the a posteriori probability of this decrease in successful predictions? They would want to do this in order have a quantitative basis (presumably a significance level) for deciding whether to continue with the service.

An aside: I’ve read that the good Reverend developed his theorem to calculate horserace probabilities.

Many thanks in advance,

Jim
Please give the problem exactly as stated in your text. This does not seem to me to be a well defined problem.

By the way, according to my son, some unscrupulous genius on Wall Street e-mailed a set of stock tips to 64,000 people. Half got advice A, and half got the exactly contrary advice. To the 32,000 who got good advice, he sent a second email. Half got advice B, and half got the contrary advice. To the 16,000 who got good advice, he sent a third email, and so on through seven sets of emails. In the eighth email sent to 1,000 people, he asked for $100 as his fee for his next set of tips. To those 1000 people, it looked as though the tipster had called right 7 times out of 7. Whether cautionary tall tale or actual fact, it's worth pondering.
 
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