Now here’s a foundation I (Bob) can get behind: Foundation for Open Access Statistics (FOAS) Their mission is to “promote free software, open access publishing, and reproducible research in statistics.” To me, that’s like supporting motherhood and apple pie! FOAS spun out of and is partially designed to support the Journal of Statistical Software (aka [...]
Archive of entries posted by
NUTS discussed on Xi’an’s Og
Xi’an’s Og (aka Christian Robert’s blog) is featuring a very nice presentation of NUTS by Marco Banterle, with discussion and some suggestions. I’m not even sure how they found Michael Betancourt’s paper on geometric NUTS — I don’t see it on the arXiv yet, or I’d provide a link.
Stan 1.3.0 and RStan 1.3.0 Ready for Action
The Stan Development Team is happy to announce that Stan 1.3.0 and RStan 1.3.0 are available for download. Follow the links on: Stan home page: http://mc-stan.org/ Please let us know if you have problems updating. Here’s the full set of release notes. v1.3.0 (12 April 2013) ====================================================================== Enhancements ———————————- Modeling Language * forward sampling (random [...]
Lame Statistics Patents
Manoel Galdino wrote in a comment off-topic on another post (which I erased): I know you commented before about patents on statistical methods. Did you know this patent (http://www.archpatent.com/patents/8032473)? Do you have any comment on patents that don’t describe mathematically how it works and how and if they’re any different from previous methods? And what [...]
Stan 1.2.0 and RStan 1.2.0
Stan 1.2.0 and RStan 1.2.0 are now available for download. See: http://mc-stan.org/ Here are the highlights. Full Mass Matrix Estimation during Warmup Yuanjun Gao, a first-year grad student here at Columbia (!), built a regularized mass-matrix estimator. This helps for posteriors with high correlation among parameters and varying scales. We’re still testing this ourselves, so [...]
How Open Should Academic Papers Be?
Richard Van Noorden reports in Nature that 95% of the authors submitting to the Nature Publishing Group choose more restrictive open-source licenses, CC-BY-NC-SA or CC-BY-NC-ND, even when given the opportunity to use a much more open license, CC-BY. (I include their data below.) How open should papers be? Should authors own their work or should [...]
Stan and RStan 1.1.0
We’re happy to announce the availability of Stan and RStan versions 1.1.0, which are general tools for performing model-based Bayesian inference using the no-U-turn sampler, an adaptive form of Hamiltonian Monte Carlo. Information on downloading and installing and using them is available as always from Stan Home Page: http://mc-stan.org/ Let us know if you have [...]
Math Talks :: Action Movies
Jonathan Goodman gave the departmental seminar yesterday (10 Dec 2012) and I was amused by an extended analogy he made. After his (very clear) intro, he said that math talks were like action movies. The overall theorem and its applications provide the plot, and the proofs provide the action scenes.
Stan at NIPS 2012 Workshop on Probabilistic Programming
If you need an excuse to go skiing in Tahoe next month, our paper on Stan as a probabilistic programming language was accepted for: Workshop on Probabilistic Programming NIPS 2012 7–8 December, 2012, Lake Tahoe, Nevada The workshop is organized by the folks behind the probabilistic programming language Church and has a great lineup of [...]
A Stan is Born
Stan 1.0.0 and RStan 1.0.0 It’s official. The Stan Development Team is happy to announce the first stable versions of Stan and RStan. What is (R)Stan? Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. It’s sort of like BUGS, but with a different language [...]
Learning Differential Geometry for Hamiltonian Monte Carlo
You can get a taste of Hamiltonian Monte Carlo (HMC) by reading the very gentle introduction in David MacKay’s general text on information theory: MacKay, D. 2003. Information Theory, Inference, and Learning Algorithms. Cambridge University Press. [see Chapter 31, which is relatively standalone and can be downloaded separately.] Follow this up with Radford Neal’s much [...]
Martyn Plummer’s Secret JAGS Blog
Martyn Plummer, the creator of the open-source, C++, graphical-model compiler JAGS (aka “Just Another Gibbs Sampler”), runs a forum on the JAGS site that has a very similar feel to the mail-bag posts on this blog. Martyn answers general statistical computing questions (e.g., why slice sampling rather than Metropolis-Hastings?) and general modeling (e.g., why won’t [...]
Geophysicist Discovers Modeling Error (in Economics)
Continuing “heckle the press” month here at the blog, I (Bob) found the following “discovery” a little overplayed by David H. Freedman, who was writing for Scientific American in the following article and blog post: Blog: Why Economic Models are Always Wrong Article: A Formula for Economic Calamity The article’s paywalled, but the blog entry [...]
Super Sam Fuld Needs Your Help (with Foul Ball stats)
I was pleasantly surprised to have my recreational reading about baseball in the New Yorker interrupted by a digression on statistics. Sam Fuld of the Tampa Bay Rays, was the subjet of a Ben McGrath profile in the 4 July 2011 issue of the New Yorker, in an article titled Super Sam. After quoting a minor-league trainer who described Fuld as “a bit of a geek” (who isn’t these days?), McGrath gets into that lovely New Yorker detail:
One could have pointed out the more persuasive and telling examples, such as the fact that in 2005, after his first pro season, with the Class-A Peoria Chiefs, Fuld applied for a fall internship with Stats, Inc., the research firm that supplies broadcasters with much of the data anad analysis that you hear in sports telecasts.
After a description of what they had him doing, reviewing footage of games and cataloguing, he said
“I thought, They have a stat for everything, but they don’t have any stats regarding foul balls.”
Why Edit Wikipedia?
Zoe Corbyn’s article for The Guardian (UK), titled Wikipedia wants more contributions from academics, and the followup discussion on Slashdot got me thinking about my own Wikipedia edits. The article quotes Dario Taraborelli, a research analyst for the Wikimedia Foundation, as saying “Academics are trapped in this paradox of using Wikipedia but not contributing,” Huh? [...]
A.I. is Whatever We Can’t Yet Automate
A common aphorism among artificial intelligence practitioners is that A.I. is whatever machines can’t currently do.
Adam Gopnik, writing for the New Yorker, has a review called Get Smart in the most recent issue (4 April 2011). Ostensibly, the piece is a review of new books, one by Joshua Foer, Moonwalking with Einstein: The Art and Science of Remembering Everything, and one by Stephen Baker Final Jeopardy: Man vs. Machine and the Quest to Know Everything (which would explain Baker’s spate of Jeopardy!-related blog posts). But like many such pieces in highbrow magazines, the book reviews are just a cover for staking out a philosophical position. Gopnik does a typically New Yorker job in explaining the title of this blog post.
Handy Matrix Cheat Sheet, with Gradients
This post is an (unpaid) advertisement for the following extremely useful resource:
- Petersen, K. B. and M. S. Pedersen. 2008. The Matrix Cookbook. Tehcnical Report, Technical University of Denmark.
It contains 70+ pages of useful relations and derivations involving matrices. What grabbed my eye was the computation of gradients for matrix operations ranging from eigenvalues and determinants to multivariate normal density functions. I had no idea the multivariate normal had such a clean gradient (see section 8).
Bleg: Automatic Differentiation for Log Prob Gradients?
We need help picking out an automatic differentiation package for Hamiltonian Monte Carlo sampling from the posterior of a generalized linear model with deep interactions. Specifically, we need to compute gradients for log probability functions with thousands of parameters that involve matrix (determinants, eigenvalues, inverses), stats (distributions), and math (log gamma) functions. Any suggestions?
“For individuals with wine training, however, we find indications of a positive relationship between price and enjoyment”
The title of this blog post quotes the second line of the abstract of Goldstein et al.’s much ballyhooed 2008 tech report, Do More Expensive Wines Taste Better? Evidence from a Large Sample of Blind Tastings. The first sentence of the abstract is Individuals who are unaware of the price do not derive more enjoyment [...]
Andy vs. the Ideal Point Model of Voting
Last week, as I walked into Andrew’s office for a meeting, he was formulating some misgivings about applying an ideal-point model to budgetary bills in the U.S. Senate. Andrew didn’t like that the model of a senator’s position was an indifference point rather than at their optimal point, and that the effect of moving away [...]