This job ad is by Aki Aalto University, University of Helsinki, and Finnish Center for Artificial Intelligence have a great probabilistic modeling community, and we’re looking for several postdocs, research fellows and doctoral students with topics including a lot of Bayesian statistics. I’m looking for a postodc and doctoral student to work on Bayesian workflow […]

**Statistical computing**category.

## Postdoc opportunity on Bayesian prediction for human-computer interfaces! In Stuttgart!

Paul “brms” Buerkner writes: At the Cluster of Excellence SimTech in Stuttgart, Germany, we are currently looking for a fully funded PostDoc (2 years) to work on Bayesian Intent Prediction for Human-Machine Collaboration, among others supervised by me (Paul-Christian Bürkner). The goal of this specific project is to contribute to the development of a new […]

## Can statistical software do better at giving warnings when you apply a method when maybe you shouldn’t?

Gaurav Sood writes: There are legions of permutation-based methods which permute the value of a feature to determine whether the variable should be added (e.g., Boruta Algorithm) or its importance. I couldn’t reason for myself why that is superior to just dropping the feature and checking how much worse the fit is or what have […]

## Stan short course in July

Jonah Gabry is teaching a Stan short course! He’s done it before and I’ve heard that it’s excellent. Here’s the information: Dates: Wed Jul 14 – Fri Jul 16 Location: online Learn Bayesian Data Analysis and Stan with Stan Developer Jonah Gabry The course consists of three main themes: Bayesian inference and computation; the Stan […]

## Neel Shah: modeling skewed and heavy-tailed data as approximate normals in Stan using the LambertW function

Neel Shah, one of Stan’s Google Summer of Code (GSOC) interns, writes: Over the summer, I will add LambertW transforms to Stan which enable us to model skewed and heavy-tailed data as approximate normals. This post motivates the idea and describes the theory of LambertW × Z random variables. Though the normal distribution is one […]

## When MCMC fails: The advice we’re giving is wrong. Here’s what we you should be doing instead. (Hint: it’s all about the folk theorem.)

In applied Bayesian statistics we often use Markov chain Monte Carlo: a family of iterative algorithms that yield approximate draws from the posterior distribution. For example, Stan uses Hamiltonian Monte Carlo. One annoying thing about these iterative algorithms is that they can take awhile, but on the plus side this spins off all sorts of […]

## Post-doc to work on developing Bayesian workflow tools

I (Aki) am looking for a post-doc to work on developing Bayesian workflow tools at Aalto University, Finland, and Finnish Center for Artificial Intelligence, in collaboration with Andrew, Dan Simpson, Paul Bürkner, Lauren Kennedy, Måns Magnusson, Stan developers, ArviZ developers, and others. The topic is related to many ideas discussed in Bayesian Workflow paper. You […]

## Network of models

Ryan Bernstein shows this demo of a prototype of the network of models visualization in Stan. This is related to the topology of models, an idea that we’ve discussed on occasion and is a key part of statistical workflow that I don’t think is handled well by existing theory or software. What Ryan is doing […]

## Webinar: Fast Discovery of Pairwise Interactions in High Dimensions using Bayes

This post is by Eric. This Wednesday, at 12 pm ET, Tamara Broderick is stopping by to talk to us about pairwise interactions in high dimensions. You can register here. Abstract Discovering interaction effects on a response of interest is a fundamental problem in medicine, economics, and many other disciplines. In theory, Bayesian methods for […]

## Another estimate of excess deaths during the pandemic.

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. […]

## 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!