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Archive of posts filed under the Statistical computing category.

The kluges of today are the textbook solutions of tomorrow.

From a response on the Stan help list: Yes, indeed, I think it would be a good idea to reduce the scale on priors of the form U(0,100) or N(0,100^2). This won’t solve all problems but it can’t hurt. If the issue is that the variance parameter can be very small in the estimation, yes, […]

Statistics is the least important part of data science

This came up already but I’m afraid the point got lost in the middle of our long discussion of Rachel and Cathy’s book. So I’ll say it again: There’s so much that goes on with data that is about computing, not statistics. I do think it would be fair to consider statistics (which includes sampling, […]

Schiminovich is on The Simpsons

OK, fine. Maybe they could work Stan on to the show next? I thought I could retire once I’d successfully inserted the phrase “multilevel regression and poststratification” into the NYT, but now I want more more more. Maybe a cage match between Stan and Mister P on the Itchy and Scratchy show?

Shlemiel the Software Developer and Unknown Unknowns

The Stan meeting today reminded me of Joel Spolsky’s recasting of the Yiddish joke about Shlemiel the Painter. Joel retold it on his blog, Joel on Software, in the post Back to Basics: Shlemiel gets a job as a street painter, painting the dotted lines down the middle of the road. On the first day […]

Doing Data Science: What’s it all about?

Rachel Schutt and Cathy O’Neil just came out with a wonderfully readable book on doing data science, based on a course Rachel taught last year at Columbia. Rachel is a former Ph.D. student of mine and so I’m inclined to have a positive view of her work; on the other hand, I did actually look […]

Uncompressing the concept of compressed sensing

I received the following email: These compressed sensing people link to Shannon’s advice. It’s refreshing when leaders of a field state that their stuff may not be a panacea. I replied: Scarily enough, I don’t know anything about this research area at all! My correspondent followed up: Meh. They proved L1 approximates L0 when design […]

EP and ABC

Expectation propagation and approximate Bayesian computation. Here are X’s comments on a paper, “Expectation-Propagation for Likelihood-Free Inference,” by Simon Barthelme and Nicolas Chopin. The paper is not new but the topic is still hot. Also there’s this paper by Maurizio Filippone and Mark Girolami on computation for Gaussian process models. I wonder how this connects […]

Scalable Stan

Bob writes: If you have papers that have used Stan, we’d love to hear about it. We finally got some submissions, so we’re going to start a list on the web site for 2.0 in earnest. You can either mail them to the list, to me directly, or just update the issue (at least until […]

Samplers for Big Science: emcee and BAT

Over the past few months, we’ve talked about modeling with particle physicists (Allen Caldwell), astrophysicists (David Hogg, who regularly comments here), and climate and energy usage modelers (Phil Price, who regularly posts here). Big Science Black Boxes We’ve gotten pretty much the same story from all of them: their models involve “big science” components that […]

Do you ever have that I-just-fit-a-model feeling?

Didier Ruedin writes: Here’s something I’ve been wondering for a while, and I thought your blog might be the right place to get the views from a wider group, too. How would you describe that feeling when—after going through the theory, collecting data, specifying the model, perhaps debugging the code—you hit enter and get the […]