JayUtah
Penultimate Amazing
"Consistent with two different hypotheses" pretty much is just a different wording "Supports both hypotheses". As such it cant favour one over the other:
Correct. What Bayesian methods let you do is set aside the qualitative aspect of objective consistency and assign degrees of a priori belief to various elements of the model, saying a priori that you believe one datum is more or less supportive of various outcomes. Naturally that quantifies a set of preconceptions that thereafter pervade everything you hope to conclude from such a model. From those priors you can determine how the shape of subsequent data alters the probability of new outcomes, under that set of preceonceptions. It's very useful in driving iterative processes that incorporate quantifiable uncertainty. It's utterly useless in determining whether limestone on the cloth means it's an authentic 1st Century burial cloth, or any other affirmative question that must be decided by production of evidence, not conjuration.
Jabba fed his model a skewed set of beliefs that naturally reflected his preconception that limestone means authenticity. The skew was so egregious that variance around the 10-percent estimate for prior probability of authenticity mattered only slightly. Naturally the model worked out the way he planned it would, suggesting -- according to him, in an objective sense -- that limestone supports authenticity more than a medieval origin.
This is how unscrupulous people have misused Bayes for decades to convey a false sense of rigor. At best the computation means, "If you already believed strongly that limestone on the cloth meant it was authentic, then it would be prudent to believe that actual discovery of limestone would tend to reinforce even a scant probability it was authentic." Bayesian methods do not determine what is objectively true or advisable; they determine only what is iteratively likely or advisable under the a priori beliefs, from step to step, as new data arises.
In actual Bayesian inference that's not considered tautological because we watch how the numbers change among several hypotheses of varying uncertainty, as we apply new data. In the logical, probative sense it's highly tautological (and therefore useless). The "If you already believed..." predicate applies throughout, and that's what constitutes, in the logical sense, either a begged question or a circular argument, depending on your taxonomy.
Further, Jabba wants to bifurcate the question so that he can sweep important details under the ... cloth. "The cloth is authentic" means a certain specific set of things. "The cloth is not authentic" can mean any number of possibly contradictory things. It is this artificially created ambiguity that Jabba is intentionally using to "educate" his guesses. Because, he says, we can't know specifically how an inauthentic cloth might possibly acquire limestone, it's proper to relegate that to a low probability. In the Bayesian sense, Jabba has simply latched onto one value of θ in L(θ|x) and assumed that the rest of the likelihood function would behave as he needed it to.
And of course Jabba assiduously does not want to discuss his priors. This is the mark of someone who isn't simply fumbling around with Bayes but someone who has had experience misapplying Bayes and therefore cognizant of its inherent weaknesses. It's one thing to realize that one's foray into math was misguided. It's another thing to realize it and keep trying to hide it. One is an innocent mistake; the other is deliberate deception.
Jabba?
Mojo is a ninja.