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Archive of posts filed under the Bayesian Statistics category.

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 […]

The likelihood principle in model check and model evaluation

(This post is by Yuling) The likelihood principle is often phrased as an axiom in Bayesian statistics. It applies when we are (only) interested in estimating an unknown parameter , and there are two data generating experiments both involving , each having observable outcomes and and likelihoods and . If the outcome-experiment pair satisfies , […]

“Inferring the effectiveness of government interventions against COVID-19”

John Salvatier points us to this article by Jan Brauner et al. that states: We gathered chronological data on the implementation of NPIs [non-pharmaceutical interventions, i.e. policy or behavioral interventions] for several European, and other, countries between January and the end of May 2020. We estimate the effectiveness of NPIs, ranging from limiting gathering sizes, […]

Literally a textbook problem: if you get a positive COVID test, how likely is it that it’s a false positive?

This post is by Phil Price, not Andrew. This will be obvious to most readers of this blog, who have seen this before and probably thought about it within the past few months, but the blog gets lots of readers and this might be new to some of you. A friend of mine just tested […]

Discussion of uncertainties in the coronavirus mask study leads us to think about some issues . . .

1. Communicating of uncertainty A member of the C19 Discussion List, which is a group of frontline doctors fighting Covid-19, asked me what I thought of this opinion article, “Covid-19: controversial trial may actually show that masks protect the wearer,” published last month by James Brophy in the British Medical Journal. Brophy writes: Paradoxically, the […]

The Shrinkage Trilogy: How to be Bayesian when analyzing simple experiments

There are lots of examples of Bayesian inference for hierarchical models or in other complicated situations with lots of parameters or with clear prior information. But what about the very common situation of simple experiments, where you have an estimate and standard error but no clear prior distribution? That comes up a lot! In such […]

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond

Charles Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal write: Gaussian latent variable models are a key class of Bayesian hierarchical models with applications in many fields. Performing Bayesian inference on such models can be challenging as Markov chain Monte Carlo algorithms struggle with the geometry of the resulting posterior distribution and can be prohibitively slow. […]

“We’ve got to look at the analyses, the real granular data. It’s always tough when you’re looking at a press release to figure out what’s going on.”

Chris Arderne writes: Surprised to see you hadn’t yet discussed the Oxford/AstraZeneca 60%/90% story on the blog. They accidentally changed the dose for some patients without an hypothesis, saw that it worked out better and are now (sort of) claiming 90% as a result… Sounds like your kind of investigation? I hadn’t heard about this […]

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