Elliott Morris points us to this set of estimates by Sondre Solstad of excess deaths during the pandemic. The above graph is for the whole world; they also have separate graphs by continent and by country. From the description: The Economist’s global excess-death-toll estimates are, as far as we know, the first of their kind. […]

**Statistical computing**category.

## What did ML researchers talk about in their broader impacts statements?

This is Jessica. A few months back I became fascinated with the NeurIPS broader impact statement “experiment” where NeurIPS organizers asked all authors to in some way address the broader societal implications of their work. It’s an interesting exercise in requiring researchers to make predictions under uncertainty about societal factors they might not be used […]

## Formalizing questions about feedback loops from model predictions

This is Jessica. Recently I asked a question about when a model developer should try to estimate the relationship between model predictions and the observed behavior that results when people have access to the model predictions. Kenneth Tay suggested a recent machine learning paper on Performative Prediction by Juan Perdomo Tijana Zrnic. Celestine Mendler-Dunner and […]

## “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 […]

## 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 […]

## 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 […]

## The Folk Theorem, revisited

It’s time to review the folk theorem, an old saw on this blog, on the Stan forums, and in all of Andrew’s and my applied modeling. Folk Theorem Andrew uses “folk” in the sense of being folksy as opposed to rigorous. The Folk Theorem of Statistical Computing (Gelman 2008): When you have computational problems, often […]

## Work on Stan as part of Google’s Summer of Code!

The Stan project is excited to announce that we will be participating in Google Summer of Code (GSoC) 2021 as a mentoring organization under the NumFOCUS umbrella. GSoC is an initiative that connects students with open source projects to give them hands-on experience working on open source code. We are thrilled to offer three projects […]

## 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 […]

## Summer research jobs at Flatiron Institute

If you’re an undergrad or grad student and work in applied math, stats, or machine learning, you may be interested in our summer research assistant and associate positions at the Flatiron Institute’s Center for Computational Mathematics: Scientific computing summer positions Machine learning and statistics summer positions There is no deadline, but we’ll start reviewing applications […]

## 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!

## Simulated-data experimentation: Why does it work so well?

Someone sent me a long question about a complicated social science problem involving intermediate outcomes, errors in predictors, latent class analysis, path analysis, and unobserved confounders. I got the gist of the question but it didn’t quite seem worth chasing down all the details involving certain conclusions to be made if certain affects disappeared in […]

## Computation+Journalism 2021 this Friday

This post is by Jessica. Last year I was program chair for Computation+Journalism, a conference that brings together computer scientists and other researchers with journalists to brainstorm about the future of journalism. I spent a bunch of time organizing a program around the theme of uncertainty communication and then massive uncertainty due to covid-19 hit […]

## 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 […]

## 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 […]

## 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 […]

## 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 […]

## “I Can’t Believe It’s Not Better”

Check out this session Saturday at Neurips. It’s a great idea, to ask people to speak on methods that didn’t work. I have a lot of experience with that! Here are the talks: Max Welling: The LIAR (Learning with Interval Arithmetic Regularization) is Dead Danielle Belgrave: Machine Learning for Personalised Healthcare: Why is it not […]

## 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. […]

## 2 PhD student positions on Bayesian workflow! With Paul Bürkner!

Paul Bürkner writes: The newly established work group for Bayesian Statistics of Dr. Paul-Christian Bürkner at the Cluster of Excellence SimTech, University of Stuttgart (Germany), is looking for 2 PhD students to work on Bayesian workflow and Stan-related topics. The positions are fully funded for at least 3 years and people with a Master’s degree […]