To claim evidence that I'm overconfident, you have to show me asserting something that is wrong, and then failing to update when you provide evidence that it's wrong.
In the thread which you referenced, I asked you questions, and the only thing I asserted was that EM and Gibbs sampling find priors which will result in computed posteriors being well-calibrated to the data. You did not provide, and still have not provided, evidence that that statement was wrong. Therefore I did not exhibit a failure to update
I might be using different terminology than you--by "priors" I meant the values that I'm going to use as priors in my running program on new data for transferred function annotations, and by "posteriors" I meant the posterior probability it will compute for a given annotation, given those "priors". I didn't claim to know what the standard terminology is. The only thing I claimed was that Gibbs sampling & EM did something that, using my terminology, could be described as setting priors so they gave calibrated results.
If you had corrected my terminology, and I'd ignored you, that would have been a failure to update. If you'd explained that I misunderstand Gibbs sampling, that would have been a failure to update. You didn't.
Relevant to your post? I don't know. I didn't assert that that particular fact was relevant to your post. I don't know if I even read your post. I responded to your comment, "seek a prior that guarantees posterior calibration," very likely in an attempt to understand your post.
you didn't know what I was talking about, but you thought you did
Again, what are you talking about? I asked you questions. The only thing I claimed to know was about the subject that I brought up, which was EM and Gibbs sampling.
As far as I can see, I didn't say anything confidently, I didn't say anything that was incorrect AFAIK, I didn't claim you had made a mistake, and I didn't fail to update on any evidence that something I'd said was wrong. So all these words of yours are not evidence for my over-confidence.
Even now, after writing paragraphs on the subject, you haven't tried to take anything I claimed and explain why it is wrong!
Try this approach: Look over the comments that you provided as evidence of my overconfidence. Say what I would have written differently if I were not overconfident.
In a fully Bayesian analysis, there will always be a top-level prior that is chosen only on the basis of prior information, not data. Any approach that uses the data to set the prior at the top level is an empirical Bayes approach.
I don't see how distinction makes sense for Gibbs sampling or EM. They are iterative procedures that take your initial (top-level) prior, and then converge on a posterior-to-the-data value (which I called the prior, as it is plugged into my operating program as a prior). It doesn't matter how you choose your initial prior; the algorithm will converge onto the same final result, unless there is some difficulty converging. That's why these algorithms exist--they spare you from having to choose a prior, if the data is strong enough that the choice makes no difference.
If you'd explained that I misunderstand Gibbs sampling, that would have been a failure to update. You didn't.
I wrote a comment that was so discordant with your understanding of Gibbs sampling and EM that it should have been a red flag that one or the other of us was misunderstanding something. Instead you put forth a claim stating your understanding, and it fell to me to take note of the discrepancy and ask for clarification. This failure to update is the exact event which prompted me to attach "Dunning-Kruger" to my understanding of you.
...I d
[Summary: Trying to use new ideas is more productive than trying to evaluate them.]
I haven't posted to LessWrong in a long time. I have a fan-fiction blog where I post theories about writing and literature. Topics don't overlap at all between the two websites (so far), but I prioritize posting there much higher than posting here, because responses seem more productive there.
The key difference, I think, is that people who read posts on LessWrong ask whether they're "true" or "false", while the writers who read my posts on writing want to write. If I say something that doesn't ring true to one of them, he's likely to say, "I don't think that's quite right; try changing X to Y," or, "When I'm in that situation, I find Z more helpful", or, "That doesn't cover all the cases, but if we expand your idea in this way..."
Whereas on LessWrong a more typical response would be, "Aha, I've found a case for which your step 7 fails! GOTCHA!"
It's always clear from the context of a writing blog why a piece of information might be useful. It often isn't clear how a LessWrong post might be useful. You could blame the author for not providing you with that context. Or, you could be pro-active and provide that context yourself, by thinking as you read a post about how it fits into the bigger framework of questions about rationality, utility, philosophy, ethics, and the future, and thinking about what questions and goals you have that it might be relevant to.