21–30 of 52 posts
Frankly, just solve an important problem with BNNs in a meaningful way with advantages that only BNNs bring (e.g uncertainty estimation) and you won’t have to write blogposts defending them.
The paragraph beginning with "Let’s consider how we might apply the Bayesian framework..." where he introduces the notation is a great example of everything I hate about mathematical notation. We have big-F, small-f, f-of-x, f-sub-x, f-star, big-F-star... and then he decides to abbreviate what he just introduced. If I didn't know what's happening and I was trying to understand this for the first time, I would have no…
The author starts with: P(A|B) = P(B|A)P(B)/P(A) This is Bayes the wrong way around. The last part should be P(A)/P(B). I have no hand in the dispute (reading here for the first time about it), but not getting the basics right is not very convincing.
Author here. The blog post contains a clear explanation of Bayesian Inference and provides careful arguments about its potential benefits and limitations. Further, Bayes rule was typed correctly when it was being used to show that the posterior q(f^*|D) \approx q(f_\theta|D). We understand Bayes rule... just had a typo and when proof reading the article we didn't check the first equation because: Who would mess up Ba…
Earlier quoted context omitted.
It's not "not getting the basics right", it's a simple typo. A thing like that shouldn't invalidate a whole article (unless you have skin in the game for the opponent argument). Why does HN have a pattern of dismissing whole articles due to simple typos? It's as if we're so habituated to skim and do tldr-reading that our brain is working overdrive to find the slightest excuse not to have to do any type of reading bey…
> unless you have skin in the game for the opponent argument I stated explicitly otherwise (I'm not even in research), something you certainly couldn't miss. I'm not quite sure, if your allegation backfires. Especially in hot temper, people make errors. But just then they should avoid making an easy target. The authors site also has some problems with rendering math (some does, some shows still dollar signs) and this…
The paragraph beginning with "Let’s consider how we might apply the Bayesian framework..." where he introduces the notation is a great example of everything I hate about mathematical notation. We have big-F, small-f, f-of-x, f-sub-x, f-star, big-F-star... and then he decides to abbreviate what he just introduced. If I didn't know what's happening and I was trying to understand this for the first time, I would have no…
By using this notation he is drawing careful distinctions between the various approximations he's using. I think it's pretty good writing.
The paragraph beginning with "Let’s consider how we might apply the Bayesian framework..." where he introduces the notation is a great example of everything I hate about mathematical notation. We have big-F, small-f, f-of-x, f-sub-x, f-star, big-F-star... and then he decides to abbreviate what he just introduced. If I didn't know what's happening and I was trying to understand this for the first time, I would have no…
The reason those are all "f"s is that they are all versions of the same thing: the function mapping features to outputs, or approximations of it. The capital "F"s refer to random variables/processes describing the same function (using capitals for RVs and lower-case for samples is standard practice in statistics). By using this notation he is drawing careful distinctions between the various approximations he's using.…
- some simple 1d plots to illustrate your points would be very illuminating; without them, i'm not very convinced by your arguments
- 'generalization-agnostic' is very frustrating as a term, i'm sure you can think of something clearer
- i'm not sure that your argument 'a BNN is only as good as its prior' is any better than 'a NN is only as good as its initialisation', yes any NN model is for sure a victim of local optima, but for most practioners this is good enough
Earlier quoted context omitted.
Author here. The blog post contains a clear explanation of Bayesian Inference and provides careful arguments about its potential benefits and limitations. Further, Bayes rule was typed correctly when it was being used to show that the posterior q(f^*|D) \approx q(f_\theta|D). We understand Bayes rule... just had a typo and when proof reading the article we didn't check the first equation because: Who would mess up Ba…
Is the posterior really an update of our prior? That doesn't make any sense to me. It's P(A|B). I can't then use it as P(A) in another inference based on different observations. What am I missing about your description of Bayesian inference?
Earlier quoted context omitted.
> unless you have skin in the game for the opponent argument I stated explicitly otherwise (I'm not even in research), something you certainly couldn't miss. I'm not quite sure, if your allegation backfires. Especially in hot temper, people make errors. But just then they should avoid making an easy target. The authors site also has some problems with rendering math (some does, some shows still dollar signs) and this…
Author here. Sorry that the typo and the render errors affected you so much. We don't see any rendering issues on our end, if you tell us what browser you are using maybe we can replicate and fix them.
It seems to concern some commentators here to a much greater extent, concluding from the whole downvoting dance. I'm old-school, I received my master in mathematics more than 25 years ago. Being in stochastics, I simply spotted an error and also some dispute, the latter from the context in the article. I want to mention, that in the past people had considered a behavior like mine as helpful, rigour was a value and especially when under fire, people were expected to try even harder.
> what browser you are using maybe we can replicate and fix them
FF 72.0.1 on Win 10 here. If it helps, in the following sequence:
--- snip ---
Bayes’s Rule simply says that for any two non-independent random variables $A$ and $B$, seeing that $B$ took a specific value $b$ changes the distribution of the random variable $A$. In standard lingo, the term Pr(A=a) is called the prior, Pr(B=b∣A=a) is the likelihood, and Pr(A=a∣B=b) is the posterior.
---snap---
all the single capitals (A,B...) appear embraced with dollar signs, all the Pr(...) expression are correct (even after removing uBlock/noScript restrictions)
The paragraph beginning with "Let’s consider how we might apply the Bayesian framework..." where he introduces the notation is a great example of everything I hate about mathematical notation. We have big-F, small-f, f-of-x, f-sub-x, f-star, big-F-star... and then he decides to abbreviate what he just introduced. If I didn't know what's happening and I was trying to understand this for the first time, I would have no…
This blog post is a response to ongoing discussion with the Bayesian community, so it was primarily aimed at a more technical audience. If you have any suggestions for how to make the writing more accessible, without becoming so overly expository that the mathematically-robust folk lose interest, I would love to discuss them.