I've added a link to listen on Apple Podcasts.
Sorry - YouTube's taking an abnormally long time to process the video.
Is there going to be some sort of slack or discord for attendees?
What are the two other mechanisms of action?
In my post, I didn't require the distribution over meanings of words to be uniform. It could be any distribution you wanted - it just resulted in the prior ratio of "which utterance is true" being 1:1.
Is this just the thing where evidence is theory-laden? Like, for example, how the evidentiary value of the WHO report on the question of COVID origins depends on how likely one thinks it is that people would effectively cover up a lab leak?
To be clear, this is an equivalent way of looking at normal prior-ful inference, and doesn't actually solve any practical problem you might have. I mostly see it as a demonstration of how you can shove everything into stuff that gets expressed as likelihood functions.
Why wouldn't this construction work over a continuous space?
Thanks for finding this! Will link it in the transcript.
Update: there's now a YouTube link