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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 […]
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 […]
The following example is interesting, not because it is particularly noteworthy but rather because it represents business as usual in much of social science: researchers trying their best, but hopelessly foiled by their use of crude psychological theories and cruder statistics, along with patterns of publication and publicity that motivate the selection and interpretation of [...]
Seth Flaxman and others at Imperial College London are using Stan to model coronavirus progression; see here (and I’ve heard they plan to fix the horrible graphs!) and this Github page. They also pointed us to this article from December 2019, Contemporary statistical inference for infectious disease models using Stan, by Anastasia Chatzilena et al. […]
According to Laure Wynants et al Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection most of the recent published studies on prediction of Covid19 are of rather low quality. Information is desperately needed but not misleading information :-( Conclusion: COVID-19 related prediction models for diagnosis and prognosis are […]
Rex Douglass writes: I direct the Machine Learning for Social Science Lab at the Center for Peace and Security Studies, UCSD. I’ve been struggling with how non-epidemiologists should contribute to COVID-19 questions right now, and I wrote a short piece that summarizes my thoughts. 8 data science suggestions For people who want to use theories […]
This comment from Ben reminded me that lots of people are running nonlinear regressions using least squares and other unstable methods of point estimation. You can do better, people! Try stan_lmer, which fits nonlinear models and also allows parameters to vary by groups. I think people have the sense that maximum likelihood or least squares […]
Eric Brown asks: How does Stan and its Bayesian modeling relate to structural equation modeling? Do you know of a resource that attempts to explain the concepts behind SEM in terms of Stan nomenclature and concepts? Some research that I’ve looked into uses SEM to evaluate latent factors underlying multiple measurements with associated errors; or […]
I’m sitting at this talk where Thomas Richardson is talking about testing the hypothesis regarding a joint distribution of three variables, X1, X2, X3. The hypothesis being tested is that X1 and X2 are conditionally independent given X3. I don’t have a copy of Richardson’s slides, but here’s a paper that I think it related, […]
Daniel Lakeland writes: COVID-19 is tested for using real-time reverse-transcriptase PCR (rt-rt-PCR). This is basically just a fancy way of saying they are detecting the presence of the RNA by converting it to DNA and amplifying it. It has already been shown by people in Israel that you can combine material from at least 64 […]
In the replication crisis in science, replications have often been performed of controversial studies on silly topics such as embodied cognition, extra-sensory perception, and power pose. We’ve been talking recently about replication being something we do for high-quality studies on important topics. That is, the point of replication is not the hopeless endeavor of convincing […]

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