Sorry I don’t want to keep you here forever
Hahaha, I’m just bored waiting for a model to run.
What I don’t fully believe is the assumption on particular values of the data I provide. I though this was pretty common in Bayes, the fact that you can input information but that information can be “negated” IF there is enough information within the data you are inferring.
This seems fine.
And how do I provide that prior information was the point, in a way its information content is fairly balanced with the mixed data I use for inference
This is what I don’t agree with. You don’t get to choose how much information moves around. Bayes rule does that under the modeling assumptions you make :D. If you limit that flow of information somehow, presumably you’re doing it because you know about some outside disagreement between the two models. And I’m using the term model misspecification for this (I think it’s right).
Model misspecification is a thing @betanalpha brings up a lot around here.
I think it boils down to, if you generate data with one model and then try to fit it with another, then your posteriors (even if your MCMC chains are super healthy and returning lots of nice looking samples) can be really wrong and misleading.
It came up in this thread: Dealing with uncertain parameters that aren't purely fitting parameters . For the tldr; search for the Bob and Betancourt posts first.