As Robin’s explained below Bayesianism doesn’t do that. You should also see the works of Lakatos and Quine where they discuss the idea that falsification is flawed because all claims have auxiliary hypotheses and one can’t falsify any hypothesis in isolation even if you are trying to construct a neo-Popperian framework.
Yes, but that still doesn’t show falsificationism to be wrong, as opposed to “narrow” or “insufficiently generalized”. Lakatos and Quine have also failed to show how it’s a problem that you can’t rigidly falsifiy a hypothesis in isolation: Just as you can generalize Popper’s binary “falsified vs. unfalsified” to probabilistic cases, you can construct a Bayes net that shows how your various beliefs (including the auxiliary hypotheses) imply particular observations.
The relative likelihoods they place on the observations allow you to know the relative amount by which those various beliefs are attenuated or amplified by any particular observation. This method gives you the functional equivalent of testing hypotheses in isolation, since some of them will be attenuated the most.
As Robin’s explained below Bayesianism doesn’t do that. You should also see the works of Lakatos and Quine where they discuss the idea that falsification is flawed because all claims have auxiliary hypotheses and one can’t falsify any hypothesis in isolation even if you are trying to construct a neo-Popperian framework.
Yes, but that still doesn’t show falsificationism to be wrong, as opposed to “narrow” or “insufficiently generalized”. Lakatos and Quine have also failed to show how it’s a problem that you can’t rigidly falsifiy a hypothesis in isolation: Just as you can generalize Popper’s binary “falsified vs. unfalsified” to probabilistic cases, you can construct a Bayes net that shows how your various beliefs (including the auxiliary hypotheses) imply particular observations.
The relative likelihoods they place on the observations allow you to know the relative amount by which those various beliefs are attenuated or amplified by any particular observation. This method gives you the functional equivalent of testing hypotheses in isolation, since some of them will be attenuated the most.
Right, I was speaking in a non-Bayesian context.