Category: Bayesian Statistics

Laplace Calling

Laplace calling to the faraway towns Now war is declared and battle come down Laplace calling to the underworld Come out of the sample, you boys and girls Laplace calling, now don’t look to us Phony Bayesmania has bitten the dust Laplace calling, see we ain’t got no swing Except for the ring of that […]

Calibration and sharpness?

I really liked this paper, and am curious what other people think before I base a grant application around applying Stan to this problem in a machine-learning context. Gneiting, T., Balabdaoui, F., & Raftery, A. E. (2007). Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(2), 243–268. Gneiting […]

Bayesian post-selection inference

Richard Artner, Francis Tuerlinckx, and Wolf Vanpaemel write: We are currently researching along the lines of model selection/averaging/misspecification and post-selection inference. As far as we understand your approach to Bayesian statistical analysis looks (drastically simplified) like this: 1. A series of models is sequentially fitted (with an increase in model complexity) whereby the types of […]

Beyond Power Calculations: Some questions, some answers

Brian Bucher (who describes himself as “just an engineer, not a statistician”) writes: I’ve read your paper with John Carlin, Beyond Power Calculations. Would you happen to know of instances in the published or unpublished literature that implement this type of design analysis, especially using your retrodesign() function [here’s an updated version from Andy Timm], […]

Bayesian Computation conference in January 2020

X writes to remind us of the Bayesian computation conference: – BayesComp 2020 occurs on 7-10 January 2020 in Gainesville, Florida, USA – Registration is open with regular rates till October 14, 2019 – Deadline for submission of poster proposals is December 15, 2019 – Deadline for travel support applications is September 20, 2019 – […]

“Beyond ‘Treatment Versus Control’: How Bayesian Analysis Makes Factorial Experiments Feasible in Education Research”

Daniel Kassler, Ira Nichols-Barrer, and Mariel Finucane write: Researchers often wish to test a large set of related interventions or approaches to implementation. A factorial experiment accomplishes this by examining not only basic treatment–control comparisons but also the effects of multiple implementation “factors” such as different dosages or implementation strategies and the interactions between these […]

Here are some examples of real-world statistical analyses that don’t use p-values and significance testing.

Joe Nadeau writes: I’ve followed the issues about p-values, signif. testing et al. both on blogs and in the literature. I appreciate the points raised, and the pointers to alternative approaches. All very interesting, provocative. My question is whether you and your colleagues can point to real world examples of these alternative approaches. It’s somewhat […]

For each parameter (or other qoi), compare the posterior sd to the prior sd. If the posterior sd for any parameter (or qoi) is more than 0.1 times the prior sd, then print out a note: “The prior distribution for this parameter is informative.”

Statistical models are placeholders. We lay down a model, fit it to data, use the fitted model to make inferences about quantities of interest (qois), check to see if the model’s implications are consistent with data and substantive information, and then go back to the model and alter, fix, update, augment, etc. Given that models […]

Conditional probability and police shootings

A political scientist writes: You might have already seen this, but in case not: PNAS published a paper [Officer characteristics and racial disparities in fatal officer-involved shootings, by David Johnson, Trevor Tress, Nicole Burkel, Carley Taylor, and Joseph Cesario] recently finding no evidence of racial bias in police shootings: Jonathan Mummolo and Dean Knox noted […]

Multilevel Bayesian analyses of the growth mindset experiment

Jared Murray, one of the coauthors of the Growth Mindset study we discussed yesterday, writes: Here are some pointers to details about the multilevel Bayesian modeling we did in the Nature paper, and some notes about ongoing & future work. We did a Bayesian analysis not dissimilar to the one you wished for! In section […]

“This is a case where frequentist methods are simple and mostly work well, and the Bayesian analogs look unpleasant, requiring inference on lots of nuisance parameters that frequentists can bypass.”

Nick Patterson writes: I am a scientist/data analyst, still working, who has been using Bayesian methods since 1972 (getting on for 50 years), I was initially trained at the British code-breaking establishment GCHQ, by intellectual heirs of Alan Turing. I’ve been accused of being a Bayesian fanatic, but in fact a good deal of my […]

The Economist does Mister P

Elliott Morris points us to this magazine article, “If everyone had voted, Hillary Clinton would probably be president,” which reports: Close observers of America know that the rules of its democracy often favour Republicans. But the party’s biggest advantage may be one that is rarely discussed: turnout is just 60%, low for a rich country. […]

a hatchet job [book review]

By happenstance, I came across a rather savage review of John Hartigan’s Bayes Theory (1984) written by Bruce Hill in HASA, including the following slivers: “By and large this book is at its best in developing the mathematical consequences of the theory and at its worst when dealing with the underlying ideas and concepts, which […]