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Archive of posts filed under the Statistical computing category.

I love it when I can respond to a question with a single link

Shira writes: This came up from trying to help a colleague of mine at Human Rights Watch. He has several completely observed variables X, and a variable with 29% missing, Y. He wants a histogram (and other descriptive statistics) of a “filled in” Y. He can regress Y on X, and impute missing Y’s from […]

No, Michael Jordan didn’t say that!

The names are changed, but the song remains the same. First verse. There’s an article by a journalist, The odds, continually updated, by F.D. Flam in the NY Times to which Andrew responded in blog form, No, I didn’t say that, by Andrew Gelman, on this blog. Second verse. There’s an article by a journalist, […]

Stan 2.5, now with MATLAB, Julia, and ODEs

As usual, you can find everything on the Stan Home Page. Drop us a line on the stan-users group if you have problems with installs or questions about Stan or coding particular models. New Interfaces We’d like to welcome two new interfaces: MatlabStan by Brian Lau, and  Stan.jl (for Julia) by Rob Goedman. The new […]

Statistical Communication and Graphics Manifesto

Statistical communication includes graphing data and fitted models, programming, writing for specialized and general audiences, lecturing, working with students, and combining words and pictures in different ways. The common theme of all these interactions is that we need to consider our statistical tools in the context of our goals. Communication is not just about conveying […]

My course on Statistical Communication and Graphics

We will study and practice many different aspects of statistical communication, including graphing data and fitted models, programming in Rrrrrrrr, writing for specialized and general audiences, lecturing, working with students and colleagues, and combining words and pictures in different ways. You learn by doing: each week we have two classes that are full of student […]

Some general principles of Bayesian data analysis, arising from a Stan analysis of John Lee Anderson’s height

God is in every leaf of every tree. The leaf in question today is the height of journalist and Twitter aficionado Jon Lee Anderson, a man who got some attention a couple years ago after disparaging some dude for having too high a tweets-to-followers ratio. Anderson called the other guy a “little twerp” which made […]

What does CNN have in common with Carmen Reinhart, Kenneth Rogoff, and Richard Tol: They all made foolish, embarrassing errors that would never have happened had they been using R Markdown

Rachel Cunliffe shares this delight: Had the CNN team used an integrated statistical analysis and display system such as R Markdown, nobody would’ve needed to type in the numbers by hand, and the above embarrassment never would’ve occurred. And CNN should be embarrassed about this: it’s much worse than a simple typo, as it indicates […]

Bayesian Cognitive Modeling  Examples Ported to Stan

There’s a new intro to Bayes in town. Michael Lee and Eric-Jan Wagenmaker. 2014. Bayesian Cognitive Modeling: A Practical Course. Cambridge Uni. Press. This book’s a wonderful introduction to applied Bayesian modeling. But don’t take my word for it — you can download and read the first two parts of the book (hundreds of pages […]

My talk with David Schiminovich this Wed noon: “The Birth of the Universe and the Fate of the Earth: One Trillion UV Photons Meet Stan”

This talk will have two parts. (1) Astronomy professor David Schiminovich will discuss the ways in which recent large-scale sky surveys that include billions of data points can address questions such as, What will happen to the Earth and other planets when the Sun becomes a white dwarf? (2) Statistics professor Andrew Gelman will discuss […]

Dave Blei course on Foundations of Graphical Models

Dave Blei writes: This course is cross listed in Computer Science and Statistics at Columbia University. It is a PhD level course about applied probabilistic modeling. Loosely, it will be similar to this course. Students should have some background in probability, college-level mathematics (calculus, linear algebra), and be comfortable with computer programming. The course is […]