Consulting a dataset and counting the number of times the event occured and so on would be a rather frequentist way of doing things. If you are a Bayesian, you are supposed to have a probability estimate for any arbitrary hypothesis that's presented to you. You cannot say that oh, I do not have the dataset with me right now, can I get back to you later?
What I was expecting as a reply to my question was something along the following lines. One would first come up with a prior for the hypothesis that the world will be nuked before 2020. Then, one would identify some facts that could be used as evidence in favour or against the hypothesis. And then one would do the necessary Bayesian updates.
I know how to do this for the simple cases of balls in a bin etc. But I get confused when it comes to forming beliefs about statements that are about the real world.
If you haven't already, you might want to take a look at Bayes Theorem by Eliezer.
As sort of a quick tip about where you might be getting confused: you summarize the steps involved as (1) come up with a prior, (2) identify potential evidence, and (3) update on the evidence. You're missing one step. You also need to check to see whether the potential evidence is "true," and you need to do that before you update.
If you check out Conservation of Expected Evidence, linked above, you'll see why. You can't update just because you've thought of some... (read more)
You know what to do.
This thread is for the discussion of Less Wrong topics that have not appeared in recent posts. If a discussion gets unwieldy, celebrate by turning it into a top-level post.