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Archive of posts filed under the Bayesian Statistics category.

Can someone build a Bayesian tool that takes into account your symptoms and where you live to estimate your probability of having coronavirus?

Carl Mears writes: I’m married to a doctor who does primary care with a mostly disadvantaged patient base. The problem her patients face is if they get tested for COVID, they are supposed to self quarantine until they get their test results, which currently takes something like a week. Also, their *family* is supposed to […]

This one’s important: Bayesian workflow for disease transmission modeling in Stan

Léo Grinsztajn, Elizaveta Semenova, Charles Margossian, and Julien Riou write: This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the COVID-19 outbreak and doing Bayesian inference. Bayesian modeling provides a principled way to quantify uncertainty and incorporate prior knowledge into the […]

In Bayesian priors, why do we use soft rather than hard constraints?

Luiz Max Carvalho has a question about the prior distributions for hyperparameters in our paper, Bayesian analysis of tests with unknown specificity and sensitivity: My reply: 1. We recommend soft rather than hard constraints when we have soft rather than hard knowledge. In this case, we don’t absolutely know that spec and sens are greater […]

Create your own community (if you need to)

Back in 1991 I went to a conference of Bayesians and I was disappointed that the vast majority seem to not be interested in checking their statistical models. The attitude seemed to be, first, that model checking was not possible in a Bayesian context, and, second, that model checking was illegitimate because models were subjective. […]

New report on coronavirus trends: “the epidemic is not under control in much of the US . . . factors modulating transmission such as rapid testing, contact tracing and behavioural precautions are crucial to offset the rise of transmission associated with loosening of social distancing . . .”

Juliette Unwin et al. write: We model the epidemics in the US at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the time-varying reproduction number (the average number of secondary infections caused by an infected person), the number of individuals that have been infected and […]

OK, here’s a hierarchical Bayesian analysis for the Santa Clara study (and other prevalence studies in the presence of uncertainty in the specificity and sensitivity of the test)

After writing some Stan programs to analyze that Santa Clara coronavirus antibody study, I thought it could be useful to write up what we did more formally so that future researchers could use these methods more easily. So Bob Carpenter and I wrote an article, Bayesian analysis of tests with unknown specificity and sensitivity: When […]

Are informative priors “[in]compatible with standards of research integrity”? Click to find out!!

A couple people asked me what I thought of this article by Miguel Ángel García-Pérez, Bayesian Estimation with Informative Priors is Indistinguishable from Data Falsification, which states: Bayesian analysis with informative priors is formally equivalent to data falsification because the information carried by the prior can be expressed as the addition of fabricated observations whose […]

Laplace’s Demon: A Seminar Series about Bayesian Machine Learning at Scale

David Rohde points us to this new seminar series that has the following description: Machine learning is changing the world we live in at a break neck pace. From image recognition and generation, to the deployment of recommender systems, it seems to be breaking new ground constantly and influencing almost every aspect of our lives. […]

Calibration and recalibration. And more recalibration. IHME forecasts by publication date

Carlos Ungil writes: The IHME released an update to their model yesterday. Using now a better model and taking into account the relaxation of mitigation measures their forecast for US deaths has almost doubled to 134k (95% uncertainty range 95k-243k). My [Ungil’s] charts of the evolution of forecasts across time can be found here. I […]

Imperial College report on Italy is now up

See here. Please share your reactions and suggestions in comments. I’ll be talking with Seth Flaxman tomorrow, and we’d appreciate all your criticisms and suggestions. All this is important not just for Italy but for making sensible models to inform policy all over the world, including here.

Bayesian analysis of Santa Clara study: Run it yourself in Google Collab, play around with the model, etc!

The other day we posted some Stan models of coronavirus infection rate from the Stanford study in Santa Clara county. The Bayesian setup worked well because it allowed us to directly incorporate uncertainty in the specificity, sensitivity, and underlying infection rate. Mitzi Morris put all this in a Google Collab notebook so you can run […]

Simple Bayesian analysis inference of coronavirus infection rate from the Stanford study in Santa Clara county

tl;dr: Their 95% interval for the infection rate, given the data available, is [0.7%, 1.8%]. My Bayesian interval is [0.3%, 2.4%]. Most of what makes my interval wider is the possibility that the specificity and sensitivity of the tests can vary across labs. To get a narrower interval, you’d need additional assumptions regarding the specificity […]

Updated Imperial College coronavirus model, including estimated effects on transmissibility of lockdown, social distancing, etc.

Seth Flaxman et al. have an updated version of their model of coronavirus progression. Flaxman writes: Countries with successful control strategies (for example, Greece) never got above small numbers thanks to early, drastic action. Or put another way: if we did China and showed % of population infected (or death rate), we’d erroneously conclude that […]

Reverse-engineering priors in coronavirus discourse

Last week we discussed the Santa Clara county study, in which 1.5% of the people tested positive for coronavirus. The authors of the study performed some statistical adjustments and summarized with a range of 2.5% to 4.2% for infection rates in the county as a whole, leading to an estimated infection fatality rate of 0.12% […]

Tracking R of COVID-19 & assessing public interventions; also some general thoughts on science

Simas Kucinskas writes:

New analysis of excess coronavirus mortality; also a question about poststratification

Uros Seljak writes: You may be interested in our Gaussian Process counterfactual analysis of Italy mortality data that we just posted. Our results are in a strong disagreement with the Stanford seropositive paper that appeared on Friday. Their work was all over the news, but is completely misleading and needs to be countered: they claim […]

MRP with R and Stan; MRP with Python and Tensorflow

Lauren and Jonah wrote this case study which shows how to do Mister P in R using Stan. It’s a great case study: it’s not just the code for setting up and fitting the multilevel model, it also discusses the poststratification data, graphical exploration of the inferences, and alternative implementations of the model. Adam Haber […]

Webinar on approximate Bayesian computation

X points us to this online seminar series which is starting this Thursday! Some speakers and titles of talks are listed. I just wish I could click on the titles and see the abstracts and papers! The seminar is at the University of Warwick in England, which is not so convenient—I seem to recall that […]

BDA FREE (Bayesian Data Analysis now available online as pdf)

Our book, Bayesian Data Analysis, is now available for download for non-commercial purposes! You can find the link here, along with lots more stuff, including: • Aki Vehtari’s course material, including video lectures, slides, and his notes for most of the chapters • 77 best lines from my course • Data and code • Solutions […]

Model building is Lego, not Playmobil. (toward understanding statistical workflow)

John Seabrook writes: Socrates . . . called writing “visible speech” . . . A more contemporary definition, developed by the linguist Linda Flower and the psychologist John Hayes, is “cognitive rhetoric”—thinking in words. In 1981, Flower and Hayes devised a theoretical model for the brain as it is engaged in writing, which they called […]