As I said, the issue can be corrected for if the number of hypotheses is known, but not if the number of possibilities is unknown

You don't need to know the number, you need to know the model (which could have infinite hypotheses in it).

Your model (hypothesis set) could be specified by an infinite number of parameters, say "all possible means and variances of a Gaussian." You can have a prior on this space, which is a density. You update the density with evidence to get a new density. This is Bayesian stats 101. Why not just go read about it? Bishop's machine learning book is good.


True, but working from a model is not an inductive method, so it can't be classified as confirmation through inductive inference which is what I'm criticizing.

Open thread, Dec. 21 - Dec. 27, 2015

by MrMind 1 min read21st Dec 2015233 comments


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