I calculated that a couple pages back, but looking back, I noticed that I made a decimal place error, so I'll do it again.
Let's say that we are members of an explorer's club. In the history of the club we have sent exactly two explorers into some particular desert. The expeditions were independent, and the explorers did know each other; in fact, let's assume that the second explorer was completely unaware of the first one (and, importantly, that he had know way of knowing that the first explorer claims to have been guided by God). Both explorers returned, both said they had become lost, and both said that God spoke the them and explained where to find water, and in fact, they found water there, and were saved.
We have two hypotheses about how the explorers found water. H1: God told them where to find it. H2: They just got lucky; that is, they found it by random chance. Our data, D, consists of two out of two explorers returning and reporting that God showed them where water would be. We wish to know how much the data supports H1 compared with H2; that is, we wish to know the weight of the evidence in favor of H1 vs H2. Our calculation requires two numbers: the probability of the data on the assumption that H1 is true, P(D|H1); and the probability of the data on the assumption that H2 is true, P(D|H2).
Assume for the sake of argument, that if God guides you to water, you'll get there and live. Then P(D|H1)=1. To calculate P(D|H2) let's assume that we know (for the sake of argument) from our knowledge of the geography and climate, that once lost, the chances of finding water by random chance is 2%. Since two independent explorers each found water, P(D|H2)=.02^2=.0004. The weight of the evidence for H1 vs H2, called the likelihood ratio, is then
P(D|H1) / P(D|H2) = 1/.0004 = 2500.
This says that the evidence is 2500 times more likely if H1 is true than if H2 is true. Or, the weight of the evidence for H1 vs. H2 is 2500.
There are two related things to realize here. The first is that we were able to calculate the weight of the evidence for H2 vs. H1 without considering the prior probability of either hypothesis. As I said earlier, the weight of the evidence is independent of the prior probabilities. Therefore, no matter how much two people disagree on the prior odds of these hypotheses, they should agree on how much the evidence should change the odds. In this case, no matter what your prior odds of H2 vs H1 are, your posterior odds should be 2500 times greater than your prior. (Note that we're using odds* now, not probabilities.)
The second thing to realize is that, since we haven't taken the prior odds of the hypotheses into account, we haven't yet calculated our updated, or posterior, odds. Your prior odds are your own business and based on whatever background information (or baggage) you bring to the problem. Therefore, your posterior odds are your own business as well. This is why I've said to Jabba that I don't care what his prior probabilities are; he can have whatever prior probabilities he wants. However, he cannot have whatever values he wants for the likelihoods (i.e., the P(D|H_) terms). Rather, for each hypothesis there has to be a model that makes a quantitative prediction of the likelihood of the data given the model, as we had in problem.
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*Odds are ratios of probabilities. For example, the prior odds of H1 vs H2 is P(H1) / P(H2).