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COVID19 Global Forecasting Kaggle

Someone pointed me to this, which might be of interest to some of you.

My best thoughts on priors

My best thoughts on priors (also the thoughts of some other contributors) are at the Prior Choice Recommendations wiki. And this more theoretical paper should be helpful too. I sent these links in response to a question from Zach Branson about priors for Gaussian processes. Jim Savage also pointed to our paper on simulation-based calibration […]

The New Yorker fiction podcast: how it’s great and how it could be improved

I was having some difficulty with radio reception on my bike a few years ago so I switched to prerecorded music and podcasts. This American Life is the best, but if I’m going a lot of places I can exhaust the supply of recent episodes. For awhile I was listening to Wait Wait which is […]

Another Bayesian model of coronavirus progression

Jon Zelner writes: Just ran across this paper [Estimating unobserved SARS-CoV-2 infections in the United States, by T. Alex Perkins, Sean Cavany, Sean Moore, Rachel Oidtman, Anita Lerch, and Marya Poterek] which I think is worth signal-boosting. I [Jon] also think that the model in here could potentially be implemented in Stan (though it might […]

Hilda Bastian and John Ioannidis on coronavirus decision making; Jon Zelner on virus progression models

1. Hilda Bastian writes: Doing nothing for which there is no strong evidence is doing something: it’s withholding public health interventions that, on the balance of what we know, could save a lot of lives and trauma – including the lives of a lot of healthcare workers. Secondly, the need for societies to be able […]

Estimates of the severity of COVID-19 disease: another Bayesian model with poststratification

Following up on our discussions here and here of poststratified models of coronavirus risk, Jon Zelner writes: Here’s a paper [by Robert Verity et al.] that I think shows what could be done with an MRP approach. From the abstract: We used individual-case data from mainland China and cases detected outside mainland China to estimate […]

Do these data suggest that UPS, Amazon, etc., should be quarantining packages?

Doug Davidson writes: I just wanted to draw your attention to this paper [Aerosol and surface stability of HCoV-19 (SARS-CoV-2) compared to SARS-CoV-1, by Neeltje van Doremalen et al.] that used Stan. They are concerned with how long the virus remains viable on different surfaces, including packaging material. I think this will become more important […]

“A Path Forward for Stan,” from Sean Talts, former director of Stan’s Technical Working Group

Sean Talts was talking about his ideas of how Stan should move forward, given anticipated developments in the probabilistic programming infrastructure. I encouraged his to write up his ideas in some sort of manifesto form, and he did so. Here it is. The title is “A Path Forward for Stan,” and it begins: Stan has […]

Breaking the feedback loop: When people don’t correct their errors

OK, so here’s the pattern: 1. Someone makes a public statement with an error, an error that advances some political or personal agenda. 2. Some other people point out the error. 3. The original author refuses to apologize, or correct the error, or thank people for pointing out the error, and sometimes they don’t even […]

“Older Americans are more worried about coronavirus — unless they’re Republican”

Philip Greengard points us to the above-titled news article by Philip Bump. The article was just fine, a reminder of modern-day political polarization. The only thing that bothered me were the graphs. I redrew them above. Here were the original versions: I see a few problems with these graphs. First, the information is duplicated because […]

“Are Relational Inferences from Crowdsourced and Opt-in Samples Generalizable? Comparing Criminal Justice Attitudes in the GSS and Five Online Samples”

Justin Pickett writes: You’ve blogged a good bit on MTurk, weighting, and model-based inference. Drawing heavily on your work (Gelman, 2007; Gelman and Carlin, 2002; Wang et al., 2015), Andrew Thompson and I [Pickett] just published a study that largely confirms your concerns about MTurk (and opt-in samples), but that also emphasizes the promise of […]

The Road Back

Paul Kedrosky points us to this news article by Liam Mannix, “Cold water poured on scientific studies based on ‘statistical cult.’” Here’s what I wrote about this when it came up last year: The whole thing seems pretty pointless to me. I agree with Kristin Sainani that the paper on MBI does not make sense. […]

Sponsor a Stan postdoc or programmer!

There’s lots of great stuff going on with Stan and related research on Bayesian workflow and computation. One way that we can do more for the community is by hosting postdocs and programmers. And one way this can happen is from corporate support. The idea is that the postdoc or programmer is working on projects […]

100 Things to Know, from Lane Kenworthy

The sociologist has this great post: Here are a hundred things worth knowing about our world and about the United States. Because a picture is worth quite a few words and providing information in graphical form reduces misperceptions, I [Kenworthy] present each of them via a chart, with some accompanying text. This is great stuff. […]

Computer-generated writing that looks real; real writing that looks computer-generated

You know that thing where you stare at a word for long enough, it starts to just look weird? The letters start to separate from each other, and you become hyper-aware of the arbitrariness of associating a concept with some specific combination of sounds? There’s gotta be a word for this. Anyway, I was reminded […]

A factor of 40 speed improvement . . . that’s not something that happens every day!

Charles Margossian and Ben Bales helped out with the Stan model for coronavirus from Riou et al. Riou reports: You guys are amazing. We implemented your tricks and it reduced computation time from 3.5 days to … 2 hours (40 times less). This is way beyond what I expected, thank you so much!

We taught a class using Zoom yesterday. Here’s what we learned.

Like many schools, Columbia is moving to online teaching for awhile to minimize the potential for virus transmission, so Merlin, Pablo, and I used Zoom for our class on applied regression and causal inference. Yesterday’s topic was chapter 11, Assumptions, diagnostics, and model evaluation. Some things about the class went well, some things could be […]

Coronavirus model update: Background, assumptions, and room for improvement

Julien Riou, coauthor of one of the models we discussed here, writes: Here is an overview of the current state of the project, so that it is easier for everyone to quickly grasp what is the potential room for improvement. Background on the epidemic: COVID-19 just passed 100,000 confirmed cases all over the world, and […]

Bernie electability update

The other day we discussed an article, “New research suggests Sanders would drive swing voters to Trump — and need a youth turnout miracle to compensate,” by David Broockman and Joshua Kalla. A commenter pointed us to a post by Seth Ackerman, “Study Showing Bernie Needs Huge Youth Turnout Is Nonsense,” which states: In the […]

Woof! for descriptive statistics

Gary Smith points to this news article in the Economist, “WOOF, CAKE, BOOM: stocks with catchy tickers beat the market,” which reports: In a study published in 2009 by Gary Smith, Alex Head and Julia Wilson of Pomona College in California, a group of people were asked to pick American public companies with “clever” tickers. […]