If I'm understanding this, they have guessed the probability of various things, including events, and assembled them to assign some kind of probability for each

If I'm understanding this, they have guessed the probability of various things, including events, and assembled them to assign some kind of probability for each possibility. So it boils down to "if our guesses about some underlying probabilities are correct, this would be the answer". I see this a lot in financial analyses too, where some sophisticated analysis is done but it's just on top of guesses. It's not really accurate to say it "concludes beyond a reasonable doubt" - they have an argument, maybe a valid and supported one, buy the "bayesian analysis" is just smoke and mirrors on top of their initial assertions. If someone has a different view of what the Baysian analysis adds, I'd be interested to understand.