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Archive of posts filed under the Bayesian Statistics category.

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 […]

More bad news for the buggy-whip manufacturers

In a news article regarding difficulties in using panel surveys to measure the unemployment rate, David Leonhardt writes: The main factor is technology. It’s a major cause of today’s response-rate problems – but it’s also the solution. For decades, survey research has revolved around the telephone, and it’s worked very well. But Americans’ relationship with […]

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 at the Simons Foundation this Wed 5pm

Anti-Abortion Democrats, Jimmy Carter Republicans, and the Missing Leap Day Babies: Living with Uncertainty but Still Learning To learn about the human world, we should accept uncertainty and embrace variation. We illustrate this concept with various examples from our recent research (the above examples are with Yair Ghitza and Aki Vehtari) and discuss more generally […]

Likelihood from quantiles?

Michael McLaughlin writes: Many observers, esp. engineers, have a tendency to record their observations as {quantile, CDF} pairs, e.g., x CDF(x) 3.2 0.26 4.7 0.39 etc. I suspect that their intent is to do some kind of “least-squares” analysis by computing theoretical CDFs from a model, e.g. Gamma(a, b), then regressing the observed CDFs against […]

Questions about “Too Good to Be True”

Greg Won writes: I manage a team tasked with, among other things, analyzing data on Air Traffic operations to identify factors that may be associated with elevated risk. I think its fair to characterize our work as “data mining” (e.g., using rule induction, Bayesian, and statistical methods). One of my colleagues sent me a link […]

Avoiding model selection in Bayesian social research

One of my favorites, from 1995. Don Rubin and I argue with Adrian Raftery. Here’s how we begin: Raftery’s paper addresses two important problems in the statistical analysis of social science data: (1) choosing an appropriate model when so much data are available that standard P-values reject all parsimonious models; and (2) making estimates and […]

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 […]

Discussion of “Maximum entropy and the nearly black object”

From 1992. It’s a discussion of a paper by Donoho, Johnstone, Hoch, and Stern. As I summarize: Under the “nearly black” model, the normal prior is terrible, the entropy prior is better and the exponential prior is slightly better still. (An even better prior distribution for the nearly black model would combine the threshold and […]

“A hard case for Mister P”

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 […]