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Archive of posts filed under the Bayesian Statistics category.

“The Firth bias correction, penalization, and weakly informative priors: A case for log-F priors in logistic and related regressions”

Sander Greenland sent me this paper that he wrote with Mohammad Ali Mansournia, which discusses possible penalty functions for penalized maximum likelihood or, equivalently, possible prior distributions for Bayesian posterior mode estimation, in the context of logistic regression. Greenland and Mansournia write: We consider some questions that arise when considering alternative penalties . . . […]

My talk today at the University of Michigan, 4pm at the Institute for Social Research

Generalizing from sample to population Andrew Gelman, Department of Statistics, Columbia University We’ve been hearing a lot about “data” recently, but data are generally a means to an end, with the goal being to learn about some population of interest. How do we generalize from sample to population? The process seems a bit mysterious, especially […]

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 […]

How do companies use Bayesian methods?

Jason May writes: I’m in Northwestern’s Predictive Analytics grad program. I’m working on a project providing Case Studies of how companies use certain analytic processes and want to use Bayesian Analysis as my focus. The problem: I can find tons of work on how one might apply Bayesian Statistics to different industries but very little […]

No, I didn’t say that!

Faye Flam wrote a solid article for the New York Times on Bayesian statistics, and as part of her research she spent some time on the phone with me awhile ago discussing the connections between Bayesian inference and the crisis in science criticism. My longer thoughts on this topic are in my recent article, “The […]

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 […]

Free Stan T-shirt to the first “little twerp” who does a (good) Bayesian analysis of Jon Lee Anderson’s height

I’d like to see a Stan implementation of the analysis presented in this comment by Gary from a year and a half ago.

Waic for time series

Helen Steingroever writes: I’m currently working on a model comparison paper using WAIC, and would like to ask you the following question about the WAIC computation: I have data of one participant that consist of 100 sequential choices (you can think of these data as being a time series). I want to compute the WAIC […]

“How to disrupt the multi-billion dollar survey research industry”

David Rothschild (coauthor of the Xbox study, the Mythical Swing Voter paper, and of course the notorious Aapor note) will be speaking Friday 10 Oct in the Economics and Big Data meetup in NYC. His title: “How to disrupt the multi-billion dollar survey research industry: information aggregation using non-representative polling data.” Should be fun! P.P.S. […]

What do you do to visualize uncertainty?

Howard Wainer writes: What do you do to visualize uncertainty? Do you only use static methods (e.g. error bounds)? Or do you also make use of dynamic means (e.g. have the display vary over time proportional to the error, so you don’t know exactly where the top of the bar is, since it moves while […]