I wouldn't say I'm a "professional" historian as in I don't possess a related degree but history is one of the few areas I am well studied. Keep in my mind, this is just an opinion and I may be completely off base. First, Bayesian probability is simply put, a reasonable expectation. This doesn't mean it's correct whenever applied so it becomes subject to interpretation. Secondly, since it's become subject to interpretation, there are two ways to view anything in this case it's the existence of Jesus. On the one hand there's the objective view stating reasonable probability as being logical and at it's extreme, most likely true. Then you have the subjective view stating that the probability will most likely correspond with personal belief. Third and finally, the topic of Jesus can be difficult simply because if you interpret any evidence of his existence as heresay due to much of it being written after the fact, you'll have no need for Bayes Theorem and even if applied, it's probability were after, not certainty. If you believe the evidence proves his existence, well then sure you could use Bayesian probability all you want but the problem becomes twofold. First, are you interpreting probablity subjectively? Are you a Christian? Is your method for constructing a prior, one of the major elements of the theorem, flawed due to personal belief? Several questions arise addressing bias but let's say you're neither a Christian nor bias. We now have the second issue which is even if you could prove the existence of Jesus, whether using Bayes Theorem or not, how would prove the authenticity of his claim as the son of God? Now you would also have to prove the probability of God and even If successful there, you're left with proving the probability of The Bible accuracy and son and so forth. In conclusion, while Mr. Carrier and I share many ideological beliefs, I believe Bayesian probability doesn't extend to everything meaning there are better methods to determine something like the probability of a historical Jesus hence Bayes Theorem is neither applicable nor effective in this context. In short, (too late), I feel applying Bayesian probability isnt needed and don't understand why Mr. Carrier felt the need to use this specifically to come to the point he did.