llasram

Neat! I work on a system with some very similar math, but a slightly different model. I really like how in bayesect making the error rates asymmetric via independent Beta priors on bidirectional errors allows the computations to be nice and symmetric.

I haven't worked these all the way through, but I'm slightly skeptical or at least confused by a few details:

1. Another way to frame P(D|B=b) would be to have the old vs new side draws be beta-binomial distributed, in which case we should then have binomial coefficients for each of the draw side probabilities for the number of possible orderings of the observations. Do they end up cancelling out somewhere? [ed: Oh yes, of course -- D includes that in each case we observe exactly one of the C(n,k) orderings.]

2. I think your expected conditional entropy code is treating the imputed new observations as independent from the past observations, though even if that's the case it may not affect it much in this model. If it does though, it might be worth explicitly unit-testing the naive vs efficient calculations to ensure they match.

Anyway, thanks for sharing!

Say you somehow have advance knowledge of the burning of the Library of Alexandria. Is it legal to steal as many books as you can from the Library prior? Of course not. Is this the only way to save unique volumes from irrevocable destruction when you have no way to convince the librarians of its imminent fate, or even to contact them at all? Yes.

I think the broad market trends are pretty suggestive -- consumers in general don't care about DRM or wouldn't see e.g. Amazon's success in the ebook business. For more specifics I think we'd need hyper-detailed data. For example, how many people consider Google's use of the broken ACS4 DRM "close enough" to DRM-free?

I'm just a consumer, but Google Play Books are in fact available for DRM-free download when so-requested by the publisher. Or least that's the reason Google gives when directly providing the book as a DRM-free download.

I agree that innumeracy of various degrees is a widespread problem, but I do think the last example is because the term "false positive rate" sounds to many people like it should mean the false discovery rate. I'm sure there are some people who do have trouble reasoning from the correct definitions, but mis-identifying/remembering the semantics of the values provided leaves no chance for succesful calculation.

That we're modeling the outcome of a coin toss as a sample space set containing at least two event elements, one of which we call "heads," and that we have a probability measure which assigns 0.5 to the subset consisting of only the "heads" event.

Worth it just for finding out about `stty -ixon`. I never would have guessed from the `stty` man page description that this option would give me back C-s and C-q to bind to something actually useful.

My take from the introduction is that the books is going to mostly be about probabilistic graphical models (PGMs).

I look forward to reading this book when finished and hope they find success with this presentation of the core ideas. As a practitioner I see a fair amount of "I have a hammer; now I just need this problem to be a nail" type thinking with regard to using off-the-shelf techniques.

In the intro to this book the authors have an example with Kalman filters. A similar example is how Latent Dirichlet Allocation (LDA) is treated by different communities. In a certain chunk of the CS-dominated topic-modeling literature and in the data science blogosphere LDA is this recieved atomic technique; a black-box tool for modeling documents. In the Stan manual, it is one fairly boring example of a mixture model, only worth talking about explicitly because so many people ask about it.

If you want more detail on this story, I found Peter Nichol's book A Voyage for Madmen on the race as a whole quite engaging.

I've been enrolled in an MS Statistics program part-time while working full-time. I'm around half-way done, and by the end it will have taken me three years total taking two classes at a time, although that includes a few extra courses beyond what the program strictly requires.

That's a really good question. The best people I've seen so far have had a willingness to rigorously align their models with reality, plus the knowledge and/or experience to know what to check. Verifying assumptions, picking evaluation metrics appropriate for the problem, checking for model interpretability, checking that model decisions are sane, and so on.

And in my experience the opposite applies as well. Having a PhD in a field providing the necessary background in theory doesn't necessarily imply the skills and experience required to produce useful models.

I think this is a good example of the problems with autodidactism. For all its flaws, structured education is what makes common knowledge common. When you study on your own, you don't know what you don't know, and there's no one there to point out your obvious-with-the-right-knowledge lapses.