## LESSWRONGLW

[anonymous]5y0

So what if p(H) = 1, p(H|A) = .4, p(H|B) = .3, and p(H|C) = .3? The evidence would suggest all are wrong. But I have also determined that A, B, and C are the only possible explanations for H. Clearly there is something wrong with my measurement, but I have no method of correcting for this problem.

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If you start with inconsistent assumptions, you get inconsistent conclusions. If you believe P(H)=1, P(A&B&C)=1, and P(H|A) etc. are all <1, then you have already made a mistake. Why are you blaming this on Bayesian confirmation theory?

2Lumifer5yH is Hypothesis. You have three: HA, HB, and HC. Let's say your prior is that they are equally probable, so the unconditional P(HA) = P(HB) = P(HC) = 0.33 Let's also say you saw some evidence E and your posteriors are P(HA|E) = 0.4, P(HB|E) = 0.3, P(HC|E) = 0.3. This means that evidence E confirms HA because P(HA|E) > P(HA). This does not mean that you are required to believe that HA is true or bet your life's savings on it.
0[anonymous]5yYou are confused. If p(H) = 1, p(H, anything) = 1 or 0, so p(H | anything) = 1 or 0, if p(anything) > 0.

# 2

If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Notes for future OT posters: