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oshfdk · (This post was last modified: 06-07-2025, 05:53 PM by oshfdk.)

(06-07-2025, 05:33 PM)Mauro wrote: You are not allowed to view links. Register or Login to view.But it's true that, many times, also the probabilities of the evidence vs. H and vs. notH are hard to pin down, even broadly. There are however some methods which can be used (ie. Laplace's rule of succession, the reference class method) which minimize subjectivity, but it's not the case to discuss them (nor Bayesian logic in general) here.

I was under impression that this thread is about testing hypotheses using Bayesian logic, so this seems on topic to me?

(06-07-2025, 05:33 PM)Mauro wrote: You are not allowed to view links. Register or Login to view.To apply Bayes one does not need a prior probability at all. Indeed, discriminating between prior and evidence is ihmo unnecessary and misleading. Start from a condition of absolutely zero knowledge, that is to say P(H) = 0.5 and P(notH) = 0.5 This is the only 'prior' one needs, and it's trivial. Then start factoring in every single piece of knowledge you have: they are all evidences now.

I'm not very familiar with the Bayesian logic nor I ever used it in practice, as far as I can remember.

Suppose I make a small 1pp mistake in assessing each of individual 100 pieces of knowledge I have. Wouldn't the error after applying the rule 100 times become enormous?

What is more, I'm not sure it's easy to apply the rule if you have 100 pieces of knowledge that may depend on one another in various ways. If I'm not mistaken, for 100 pieces of knowledge you'll need pairwise conditional probabilities to get a proper result, and an exponential increase in complexity?

I don't know if Bayesian logic is of much use in cases where there is no numeric empirical data.