The prior probability of 0.13 is wrong. That would be correct if 13% of fires resulting in fatalities of children were intentionally set by the children’s dad.
Yes, that is why I did not say that the prior probability was correct. I said it was a prior probability estimate.
If you can find demographic data that gives you a better prior probability than 13%, or would like to suggest an arbitrary constant to multiply by, go for it. I meant just to emphasize that the prior probability for Willingham’s guilt is at least an order of magnitude higher than the prior probability of Knox’s guilt.
“I meant just to emphasize that the prior probability for Willingham’s guilt is at least an order of magnitude higher than the prior probability of Knox’s guilt.”
I think you made an interesting observation. Just thought it was worth noting that the prior prob is probably too high
Another aspect of this is whether we are being fully Bayesian or not. If fully Bayesian we’d have a prior distribution for the probability. that prior might have a mean or mode at 14%, but still be pretty flat (reflecting uncertainty)
The prior probability of 0.13 is wrong. That would be correct if 13% of fires resulting in fatalities of children were intentionally set by the children’s dad.
Yes, that is why I did not say that the prior probability was correct. I said it was a prior probability estimate.
If you can find demographic data that gives you a better prior probability than 13%, or would like to suggest an arbitrary constant to multiply by, go for it. I meant just to emphasize that the prior probability for Willingham’s guilt is at least an order of magnitude higher than the prior probability of Knox’s guilt.
“I meant just to emphasize that the prior probability for Willingham’s guilt is at least an order of magnitude higher than the prior probability of Knox’s guilt.”
I think you made an interesting observation. Just thought it was worth noting that the prior prob is probably too high
Another aspect of this is whether we are being fully Bayesian or not. If fully Bayesian we’d have a prior distribution for the probability. that prior might have a mean or mode at 14%, but still be pretty flat (reflecting uncertainty)