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I am not a statistician either, apart from a few courses, but your argument starts with: P(gene|healthy) = 0.010 and P(no_gene|healthy) = 0.99 P(gene|sick) =
by pcrh 13y ago
I am not a statistician either, apart from a few courses, but your argument starts with:
P(gene|healthy) = 0.010 and P(no_gene|healthy) = 0.99
P(gene|sick) = 0.011 and P(no_gene|sick) = 0.989
My point is that in many cases there is no justification for placing any number whatsoever on a prior hypothesis. You can't simply say that the prior probability of a particular gene being involved in a disease is 1%, or 0.00001% or 10%, or whatever.
Edit: I'm not saying that Bayesian statistics is without uses, it is very useful in epidemiology, for example. However it is not appropriate for determining molecular and genetic mechanisms.
- Gravityloss 13y agoI think you misunderstand. P(gene|healthy), P(gene) and P(healthy) are something we can often measure. P(healthy|gene) is something we can calculate with the Bayes formula from the above values. More generic version: We want to know P(model|data). That's what we always want. What is the probability of some model, based on this data that we measured. But we only have P(model), P(data) and P(data|model). So we use the Bayes formula to get the answer to the interesting conditional probability. It needs all three inputs. We must estimate if we don't have some. Just presenting P(data|model) and not constraining P(model) or P(data) in any way means that we can't say anything about P(model|data). It's like if we start with x + a + b = 5. If we don't know or estimate a and b, it's impossible to say anything about x. Bayes' formula is like saying then, solve it like this: x = 5 - a - b. So, if you say, there is not justification placing any constraint on a or b - then it's just saying we clearly do not have enough data to say anything about x either. There is no way out of it.