Posts Tagged ‘ Statistical computing ’

Statistical Communication and Graphics Manifesto

October 15, 2014
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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 […] The post Statistical Communication and Graphics Manifesto appeared first on Statistical Modeling, Causal Inference,…

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My course on Statistical Communication and Graphics

October 15, 2014
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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 […] The post My course on Statistical Communication and Graphics appeared first on Statistical Modeling,…

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

September 29, 2014
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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 […]

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future of computational statistics

September 28, 2014
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future of computational statistics

I am currently preparing a survey paper on the present state of computational statistics, reflecting on the massive evolution of the field since my early Monte Carlo simulations on an Apple //e, which would take a few days to return a curve of approximate expected squared error losses… It seems to me that MCMC is […]

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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

September 19, 2014
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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 […]

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Bayesian Cognitive Modeling  Examples Ported to Stan

September 11, 2014
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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 […]

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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”

September 8, 2014
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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 […] The post My talk with David Schiminovich this Wed noon: “The Birth of the…

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Dave Blei course on Foundations of Graphical Models

August 26, 2014
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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 […] The post Dave Blei course on Foundations of Graphical Models appeared first on Statistical…

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How Many Mic’s Do We Rip

August 23, 2014
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Yakir Reshef writes: Our technical comment on Kinney and Atwal’s paper on MIC and equitability has come out in PNAS along with their response. Similarly to Ben Murrell, who also wrote you a note when he published a technical comment on the same work, we feel that they “somewhat missed the point.” Specifically: one statistic […] The post How Many Mic’s Do We Rip appeared first on Statistical Modeling, Causal…

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“A hard case for Mister P”

August 20, 2014
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Kevin Van Horn sent me an email with the above title (ok, he wrote MRP, but it’s the same idea) and the following content: I’m working on a problem that at first seemed like a clear case where multilevel modeling would be useful. As I’ve dug into it I’ve found that it doesn’t quite fit […] The post “A hard case for Mister P” appeared first on Statistical Modeling, Causal…

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