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> You can actually compute p(H|x) if you use Bayesian stats. You can't "compute" it for any useful meaning of the word "compute". You can estimate it intuitiv
by andrewla 7y ago
> You can actually compute p(H|x) if you use Bayesian stats.
You can't "compute" it for any useful meaning of the word "compute". You can estimate it intuitively, or you can try to look at how many pre-registered unpublished studies or null results have been published. Otherwise there's no way to even consider getting a grasp of what P(H) would be.
- hervature 7y ago> Otherwise there's no way to even consider getting a grasp of what P(H) would be. That's called picking a prior. Usually, pick the prior that maximizes the entropy on the given interval (zero prior knowledge). For those reading and confused. Computing: p(H|x) = p(x|H)p(H)/p(x) requires p(H) which the parent is suggesting is impossible to grasp. If we want to know the probability that a coin is biased, we can assign probabilities to each hypothesis. For some people, p(bias=1/2)=1 (Dirac distribution), others might argue that p(bias=x)=1 for x in [0,1] (uniform distribution). Others might argue its some Beta function centered around 1/2. I believe what the parent is suggesting is that choosing which original belief we have in the system is a matter of philosophy, not computation.