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Archive of posts filed under the Bayesian Statistics category.

“If you’re not using a proper, informative prior, you’re leaving money on the table.”

Well put, Rob Weiss. This is not to say that one must always use an informative prior; oftentimes it can make sense to throw away some information for reasons of convenience. But it’s good to remember that, if you do use a noninformative prior, that you’re doing less than you could.

Soil Scientists Seeking Super Model

I (Bob) spent last weekend at Biosphere 2, collaborating with soil carbon biogeochemists on a “super model.” Model combination and expansion The biogeochemists (three sciences in one!) have developed hundreds of competing models and the goal of the workshop was to kick off some projects on putting some of them together intos wholes that are […]

Question about data mining bias in finance

Finance professor Ravi Sastry writes: Let’s say we have N vectors of data, {y_1,y_2,…,y_N}. Each is used as the dependent variable in a series of otherwise identical OLS regressions, yielding t-statistics on some parameter of interest, theta: {t_1,t_2,…,t_N}. The maximum t-stat is denoted t_n*, and the corresponding data are y_n*. These are reported publicly, as […]

“The Statistical Crisis in Science”: My talk in the psychology department Monday 17 Nov at noon

Monday 17 Nov at 12:10pm in Schermerhorn room 200B, Columbia University: Top journals in psychology routinely publish ridiculous, scientifically implausible claims, justified based on “p < 0.05.” And this in turn calls into question all sorts of more plausible, but not necessarily true, claims, that are supported by this same sort of evidence. To put […]

The history of MRP highlights some differences between political science and epidemiology

Responding to a comment from Thomas Lumley (who asked why MRP estimates often seem to appear without any standard errors), I wrote: In political science, MRP always seems accompanied by uncertainty estimates. However, when lots of things are being displayed at once, it’s not always easy to show uncertainty, and in many cases I simply […]

“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 […]