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Archive of posts filed under the Bayesian Statistics category.

Whatever you’re looking for, it’s somewhere in the Stan documentation and you can just google for it.

Someone writes: Do you have link to an example of Zero-inflated poisson and Zero-inflated negbin model using pure stan (not brms, nor rstanarm)? If yes, please share it with me! I had a feeling there was something in the existing documentation already! So I googled *zero inflated Stan*, and . . . yup, it’s the […]

Responding to Richard Morey on p-values and inference

Jonathan Falk points to this post by Richard Morey, who writes: I [Morey] am convinced that most experienced scientists and statisticians have internalized statistical insights that frequentist statistics attempts to formalize: how you can be fooled by randomness; how what we see can be the result of biasing mechanisms; the importance of understanding sampling distributions. […]

Probability problem involving multiple coronavirus tests in the same household

Mark Tuttle writes: Here is a potential homework problem for your students. The following is a true story. Mid-December, we have a household with five people. My wife and myself, and three who arrived from elsewhere. Subsequently, various diverse symptoms ensue – nothing too serious, but everyone is concerned, obviously. Video conference for all five […]

“Bayesian Causal Inference for Real World Interactive Systems”

David Rohde points us to this workshop: Machine learning has allowed many systems that we interact with to improve performance and personalize. An important source of information in these systems is to learn from historical actions and their success or failure in applications – which is a type of causal inference. The Bayesian approach is […]

EU proposing to regulate the use of Bayesian estimation

The European Commission just released their Proposal for a Regulation on a European approach for Artificial Intelligence. They finally get around to a definition of “AI” on page 60 of the report (link above): ‘artificial intelligence system’ (AI system) means software that is developed with one or more of the techniques and approaches listed in […]

Hierarchical modeling of excess mortality time series

Elliott writes: My boss asks me: For our model to predict excess mortality around the world, we want to calculate a confidence interval around our mean estimate for total global excess deaths. We have real excess deaths for like 60 countries, and are predicting on another 130 or so. we can easily calculate intervals for […]

Webinar: An introduction to Bayesian multilevel modeling with brms

This post is by Eric. This Wednesday, at 12 pm ET, Paul Bürkner is stopping by to talk to us about brms. You can register here. Abstract The talk will be about Bayesian multilevel models and their implementation in R using the package brms. We will start with a short introduction to multilevel modeling and to […]

Rob Tibshirani, Yuling Yao, and Aki Vehtari on cross validation

Rob Tibshirani writes: About 9 years ago I emailed you about our new significance result for the lasso. You wrote about in your blog. For some reason I never saw that full blog until now. I do remember the Stanford-Berkeley Seminar in 1994 where I first presented the lasso and you asked that question. Anyway, […]

State-level predictors in MRP and Bayesian prior

Something came up in comments today that I’d like to follow up on. In our earlier post, I brought up an example: If you’re modeling attitudes about gun control, think hard about what state-level predictors to include. My colleagues and I thought about this a bunch of years ago when doing MRP for gun-control attitudes. […]

Some issues when using MRP to model attitudes on a gun control attitude question on a 1–4 scale

Elliott Morris writes: – I want to run a MRP model predicting 4 categories of response options to a question about gun control (multinomial logit) – I want to control for demographics in the standard hierarchical way (MRP) – I want the coefficients to evolve in a random walk over time, as I have data […]

Discuss our new R-hat paper for the journal Bayesian Analysis!

Here’s your opportunity: We welcome public contributions to the Discussion of the manuscript the manuscript Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC by A. Vehtari, A. Gelman, D. Simpson, B. Carpenter and P. C. Bürkner, which will be featured as a Discussion Paper in the June 2021 issue of the […]

More background on our research on constructing an informative prior from a corpus of comparable studies

Erik van Zwet writes: The post (“The Shrinkage Trilogy: How to be Bayesian when analyzing simple experiments”) didn’t get as many comments as I’d hoped, so I wrote an short explainer and a reading guide to help people understand what we’re up to. All three papers have the same very simple model. We abstract a […]

Many years ago, when he was a baby economist . . .

Jonathan Falk writes: Many years ago, when I was a baby economist, a fight broke out in my firm between two economists. There was a question as to whether a particular change in the telecommunications laws had spurred productivity improvements or not. There a trend of x% per year in productivity improvements that had gone […]

Priors for counts in binomial and multinomial models

Frank Tuyl writes: I was hoping to get your opinion on something. I’ve always been a fan of E.T. Jaynes, so really appreciate what Larry Bretthorst did to get his book out there posthumously. But are you aware of this paper of his? A friend and colleague of mine is getting right into it and […]

How to figure out what went wrong with this model?

Tony Hu writes: Could you please help look at an example of my model fitting? I used a very flexible model—Bayesian multivariate adaptive regression spline. The result is as follows: I fitted the corn yield data with multiple predictors for counties of the US (the figure shows results of Belmont County in Ohio). My advisor […]

PhD student and postdoc positions in Norway for doing Bayesian causal inference using Stan!

Guido Biele writes: I have two positions for a postdoc and PhD student open in a project where we will use observational data from Norwegian National registries, structural models (or the potential outcomes framework, the main thing is that we want to think systematically about identification), and Bayesian estimation in Stan to estimate causal effects […]

Webinar: On Bayesian workflow

This post is by Eric. This Wednesday, at 12 pm ET, Aki Vehtari is stopping by to talk to us about Bayesian workflow. You can register here. Abstract We will discuss some parts of the Bayesian workflow with a focus on the need and justification for an iterative process. The talk is partly based on […]

Bayesian methods and what they offer compared to classical econometrics

A well-known economist who wishes to remain anonymous writes: Can you write about this agent? He’s getting exponentially big on Twitter. The link is to an econometrician, Jeffrey Wooldridge, who writes: Many useful procedures—shrinkage, for example—can be derived from a Bayesian perspective. But those estimators can be studied from a frequentist perspective, and no strong […]

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!

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