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Archive of posts filed under the Bayesian Statistics category.

The Mets are hiring

Des McGowan writes: We are looking to hire multiple full time analysts/senior analysts to join the Baseball Analytics department at the New York Mets. The roles will involve building, testing, and presenting statistical models that inform decision-making in all facets of Baseball Operations. These positions require a strong background in complex statistics and data analytics, […]

Postdoc in precision medicine at Johns Hopkins using Bayesian methods

Aki Nishimura writes: My colleague Scott Zeger and I have a postdoc position for our precision medicine initiative at Johns Hopkins and we are looking for expertise in Bayesian methods, statistical computation, or software development. Expertise in Stan would be a plus!

Postdoc in Paris for Bayesian models in genetics . . . differential equation models in Stan

Julie Bertrand writes: The BIPID team in the IAME UMR1137 INSERM Université de Paris is opening a one-year postdoctoral position to develop Bayesian approaches to high throughput genetic analyses using nonlinear mixed effect models. The candidate will analyse longitudinal phenotype data using differential equation models on clinical trial data with Stan and perform simulation studies […]

Is the right brain hemisphere more analog and Bayesian?

Oliver Schultheiss writes: I recently commented one of your posts (I forgot which one) with a reference to evidence suggesting that the right brain hemisphere may be in a better position to handle numbers and probabilistic predictions. Yesterday I came across the attached paper by Filipowicz, Anderson, & Danckert (2016) that may be of some […]

Webinar: Some Outstanding Challenges when Solving ODEs in a Bayesian context

This post is by Eric. This Wednesday, at 12 pm ET, Charles Margossian is stopping by to talk to us about solving ODEs using Bayesian methods. You can register here. If you want to get a feel for the types of issues he will be discussing, take a look at his (and Andrew’s) recent case […]

Come work with me and David Shor !

Open positions on our progressive data team: Machine Learning engineer – Software Engineer – Devops – We are a diverse team of engineers, data scientists, statisticians, and political insiders who are closely connected to some of the most important decision makers in the progressive ecosystem. We worked with central players to develop strategy and direct […]

“Maybe we should’ve called it Arianna”

Katie Hafner wrote this obituary of Arianna Rosenbluth, original programmer of what is known as the Metropolis algorithm: Arianna Rosenbluth Dies at 93; Pioneering Figure in Data Science Dr. Rosenbluth, who received her physics Ph.D. at 21, helped create an algorithm that has became a foundation of understanding huge quantities of data. She died of […]

How to track covid using hospital data collection and MRP

Len Covello, Yajuan Si, and I write: The current way we track the prevalence of coronavirus infections is deeply flawed. Ideally, health officials would test random samples of citizens in each community in a systematic way. But throughout the pandemic, the United States has lacked the political will or funding to pursue it. Instead, testing […]

New research suggests: “Targeting interventions – including transmission-blocking vaccines – to adults aged 20-49 is an important consideration in halting resurgent epidemics and preventing COVID-19-attributable deaths.”

In recent weeks we’ve been hearing a lot about the priority of vaccinations. Should we be vaccinating older people first? Essential workers? Just vaccinate as many as possible without worrying about who gets it? Giving out the vaccine is partly about protecting people and partly about slowing the chains of transmission. The results of a […]

About that claim that “SARS-CoV-2 is not a natural zoonosis but instead is laboratory derived”

A couple people pointed me to this article, “A Bayesian analysis concludes beyond a reasonable doubt that SARS-CoV-2 is not a natural zoonosis but instead is laboratory derived.” It is hard for me to assess this document, as the key issues involve the biology of the virus, and I don’t know anything about genetics. There […]

Bayesian inference completely solves the multiple comparisons problem

I’m rerunning this one from 2016 because it came up at work recently, and I think the general topic is as important as it’s ever been. flat priors consistently give bad inferences. Or, to put it another way, the routine use of flat priors results in poor frequency properties in realistic settings where studies are […]

Hierarchical stacking, part II: Voting and model averaging

(This post is by Yuling) Yesterday I have advertised our new preprint on hierarchical stacking. Apart from the methodology development, perhaps I could draw some of your attention to the analogy between model averaging/selection and voting systems, which is likely to be more entertaining. Model selection = we have multiple models to fit the data and […]

Hierarchical stacking

(This post is by Yuling) Gregor Pirš, Aki, Andrew, and I wrote: Stacking is a widely used model averaging technique that yields asymptotically optimal predictions among linear averages. We show that stacking is most effective when the model predictive performance is heterogeneous in inputs, so that we can further improve the stacked mixture by a […]

Infer well arsenic dynamic from filed kits

(This post is by Yuling, not Andrew) Rajib Mozumder, Benjamin Bostick, Brian Mailloux, Charles Harvey, Andrew, Alexander van Geen, and I arxiv a new paper “Making the most of imprecise measurements: Changing patterns of arsenic concentrations in shallow wells of Bangladesh from laboratory and field data”. Its abstract reads: Millions of people in Bangladesh drink […]

Webinar: Functional uniform priors for dose-response models

This post is by Eric. This Wednesday, at 12 pm ET, Kristian Brock is stopping by to talk to us about functional uniform priors for dose-response models. You can register here. Abstract Dose-response modeling frequently employs non-linear regression. Functional uniform priors are distributions that can be derived for parameters that convey approximate uniformity over the […]

Simulation-based calibration: Two theorems

Throat-clearing OK, not theorems. Conjectures. Actually not even conjectures, because for a conjecture you have to, y’know, conjecture something. Something precise. And I got nothing precise for you. Or, to be more precise, what is precise in this post is not new, and what is new is not precise. Background OK, first for the precise […]

Routine hospital-based SARS-CoV-2 testing outperforms state-based data in predicting clinical burden.

Len Covello, Yajuan Si, Siquan Wang, and I write: Throughout the COVID-19 pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community […]

“They adjusted for three hundred confounders.”

Alexey Guzey points to this post by Scott Alexander and this research article by Elisabetta Patorno, Robert Glynn, Raisa Levin, Moa Lee, and Krista Huybrechts, and writes: I [Guzey] am extremely skeptical of anything that relies on adjusting for confounders and have no idea what to think about this. My intuition would be that because […]

Flaxman et al. respond to criticisms of their estimates of effects of anti-coronavirus policies

As youall know, as the coronavirus has taken its path through the world, epidemiologists and social scientists have tracked rates of exposure and mortality, studied the statistical properties of the transmission of the virus, and estimated effects of behaviors and policies that have been tried to limit the spread of the disease. All this is […]

How many infectious people are likely to show up at an event?

Stephen Kissler and Yonatan Grad launched a Shiny app, Effective SARS-CoV-2 test sensitivity, to help you answer the question, How many infectious people are likely to show up to an event, given a screening test administered n days prior to the event? Here’s a screenshot. The app is based on some modeling they did with […]

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