It is a damn shame to hear of this tiredness, and I hope that your mood improves, somehow, somewhen, hopefully sooner than you expect.
This reply, though, I am forced to say, does not quite persuade, and to be totally frank even disappoints me a little. It was my understanding that both MIRI and to some extent one of your goals as of now was one of public outreach and communication (as a sub-goal for policy change) - this was at least how I understood this recent tweet describing what MIRI is doing and why people should donate to it, as well as other things you've been doing somewhat recently going on a... (read more)
Hmm. I'm sorry to have to say this, but I think this post is not very good.
In summary, I would say that this article is discussing a set of very common, well-trodden issues (Bayesian model checking/expansion/selection, generally speaking problems of insufficient 'model space') in a very nontechnical way (which is of course a fine approach on its own, but it's strange that the thing that this is talking about is not referenced or referred to as the thing it's called anywhere, instead everything here is presented as if it was a novel first-principles contribution), in such a way that proposes the most wishy-washy solution to the problem (just ignoring it) without reference... (read 415 more words →)
Neat post, thank you! Also, you seem to have posted this twice.
Maybe! But I would expect him to change his view to something like this in case you managed to persuade him that there is some crucial flaw in Bayesianism. While your goal seems to be to propagate the toolbox-view as the only valid approach. So you might as well engage with a stronger version of law-view right now.
Well, maybe, I don't know. As it stands it just seems best to argue against what he has said at his word then to assume otherwise, though, insofar as other people take this view at face value. Though, if such a thing does come about, I would of course have to write a different post.... (read 834 more words →)
Thank you for spotting that.
Is it? Certainly Yudkowksy takes a lot of inspiration from Jaynes but I don't remember him beating on this particular drum. Of course there were arguments as to why being Bayesian is correct from a philosophical perspective, just not so much that everything must be approximating it. Though, it's been years since I read him, so I could be wrong.
You can have a law-view interpretation of such synthesis where we conceptualize Bayesianism as an imperfect approximation, a special case of the True Law, which should also capture all the good insights of Frequentism.
Yes, but such an interpretation falls outside of Yudkowsky's view, as I understand it (for example on that X thread in another comment in this post, and his comments on other statistics topics I've seen around - could fish for the quotes, but I'm a bit held up at this precise moment), which is what that I'm focusing on here.
On Walker, on that paragraph he is criticizing the specific (common) practice of comparing separate Bayesian models and picking the... (read 528 more words →)
I think you've misunderstood the point somewhat. On the question of 'taking the best from both', that is what Yudkowsky calls a "tool" view, whereas I'm trying to argue against his view of its status as a necessary law of correct reasoning (see my other comment for related points). Insofar as you acknowledge that Frequentists can produce good answers independently of there existing some Bayesian prior-likelihood combination they must be approximating, we agree.
Still, the problem of 'taking the best from both' from a philosophically Bayesian view is that it is incoherent - you can't procedurally pick a method which isn't derived through a Bayes update of your prior beliefs without incurring in... (read more)
Hello! Thank you for the comment, these are good points.
This is my first post, so forgive me for it being a bit of a carelessly referenced, informal ramble. Feedback is appreciated.
As I understand it, Yudkowsky's contends that is there exists an ideal Bayesian, with respect to which any epistemic algorithm is only 'good' insofar as it is approximating it. Specifically, this ideal Bayesian is following a procedure such that their prior is defined by a 'best' summary of their available knowledge. This is the basis for which he claims that, for instance, Einstein's reasoning must have had a strictly Bayesian component to it, otherwise he could not have been correct. He generally extends this assertion to argue for seeking that Bayesian... (read 3797 more words →)
Thanks for replying. Given that it's been a month, sadly, I don't fully remember all the details of why I wrote what I wrote in my initial comment, but I'll try to roughly rewrite my objections in a more specific way so that you get where it makes contact with your post ("if I had more time, I would've written a shorter letter"). Forgive me if it was somehow hard to understand, English is my second language.
My first issue: the post is titled "Good if make prior after data instead of before". Yet, the post's driving example is a situation where the (marginal) prior probability of what you're interested in doesn't actually... (read 698 more words →)