Chad Kiewiet De Jonge, Gary Langer, and Sofi Sinozich write: This paper presents state-level estimates of the 2016 presidential election using data from the ABC News/Washington Post tracking poll and multilevel regression with poststratification (MRP). While previous implementations of MRP for election forecasting have relied on data from prior elections to establish poststratification targets for […]

**Stan**category.

## Where do I learn about log_sum_exp, log1p, lccdf, and other numerical analysis tricks?

Richard McElreath inquires: I was helping a colleague recently fix his MATLAB code by using log_sum_exp and log1m tricks. The natural question he had was, “where do you learn this stuff?” I checked Numerical Recipes, but the statistical parts are actually pretty thin (at least in my 1994 edition). Do you know of any books/papers […]

## Anyone want to run this Bayesian computing conference in 2022?

OK, people think I’m obsessive with a blog with a 6-month lag, but that’s nothing compared to some statistics conferences. Mylène Bédard sends this along for anyone who might be interested: The Bayesian Computation Section of ISBA is soliciting proposals to host its flagship conference: Bayes Comp 2022 The expectation is that the meeting will […]

## Multilevel modeling in Stan improves goodness of fit — literally.

John McDonnell sends along this post he wrote with Patrick Foley on how they used item-response models in Stan to get better clothing fit for their customers: There’s so much about traditional retail that has been difficult to replicate online. In some senses, perfect fit may be the final frontier for eCommerce. Since at Stitch […]

## I am the supercargo

In a form of sympathetic magic, many built life-size replicas of airplanes out of straw and cut new military-style landing strips out of the jungle, hoping to attract more airplanes. – Wikipedia Twenty years ago, Geri Halliwell left the Spice Girls, so I’ve been thinking about Cargo Cults a lot. As an analogy for what […]

## Stan goes to the World Cup

Leo Egidi shares his 2018 World Cup model, which he’s fitting in Stan. But I don’t like this: First, something’s missing. Where’s the U.S.?? More seriously, what’s with that “16.74%” thing? So bogus. You might as well say you’re 66.31 inches tall. Anyway, as is often the case with Bayesian models, the point here is […]

## Stan Workshop on Pharmacometrics—Paris, 24 July 2018

What: A one-day event organized by France Mentre (IAME, INSERM, Univ SPC, Univ Paris 7, Univ Paris 13) and Julie Bertrand (INSERM) and sponsored by the International Society of Pharmacometrics (ISoP). When: Tuesday 24 July 2018 Where: Faculté Bichat, 16 rue Henri Huchard, 75018 Paris Free Registration: Registration is being handled by ISoP; please click […]

## Global shifts in the phenological synchrony of species interactions over recent decades

Heather Kharouba et al. write: Phenological responses to climate change (e.g., earlier leaf-out or egg hatch date) are now well documented and clearly linked to rising temperatures in recent decades. Such shifts in the phenologies of interacting species may lead to shifts in their synchrony, with cascading community and ecosystem consequences . . . We […]

## The Manager’s Path (book recommendation for new managers)

I (Bob) was visiting Matt Hoffman (of NUTS fame) at Google in California a few weeks ago, and he recommended the following book: Camille Fournier. 2017. The Manager’s Path. O’Reilly. It’s ordered from being an employee, to being a tech lead, to managing a small team, to managing teams of teams, and I stopped there. […]

## Stan on TV

For reals. Billions, Season 3, Episode 9 35:10.

## Boston Stan meetup 12 June!

Shane Bussmann writes to announce the next Boston/Camberville Stan users meetup, Tuesday, June 12, 2018, 6:00 PM to 9:00 PM, at Insight Data Science Office, 280 Summer St., Boston: To kick things off for our first meetup in 2018, I [Bussman] will give a talk on rating teams in recreational ultimate frisbee leagues. In this […]

## How about zero-excluding priors for hierarchical variance parameters to improve computation for full Bayesian inference?

So. For awhile now we’ve moved away from the uniform (or, worse, inverse-gamma!) prior distributions for hierarchical variance parameters. We’ve done half-Cauchy, folded t, and other options; now we’re favoring unit half-normal. We also have boundary-avoiding priors for point estimates, so that in 8-schools-type problems, the posterior mode won’t be zero. Something like the gamma(2) […]

## The current state of the Stan ecosystem in R

(This post is by Jonah) Last week I posted here about the release of version 2.0.0 of the loo R package, but there have been a few other recent releases and updates worth mentioning. At the end of the post I also include some general thoughts on R package development with Stan and the growing number of […]

## Postdoc opportunity at AstraZeneca in Cambridge, England, in Bayesian Machine Learning using Stan!

Here it is: Predicting drug toxicity with Bayesian machine learning models We’re currently looking for talented scientists to join our innovative academic-style Postdoc. From our centre in Cambridge, UK you’ll be in a global pharmaceutical environment, contributing to live projects right from the start. You’ll take part in a comprehensive training programme, including a focus […]

## You better check yo self before you wreck yo self

We (Sean Talts, Michael Betancourt, Me, Aki, and Andrew) just uploaded a paper (code available here) that outlines a framework for verifying that an algorithm for computing a posterior distribution has been implemented correctly. It is easy to use, straightforward to implement, and ready to be implemented as part of a Bayesian workflow. This type of […]

## loo 2.0 is loose

This post is by Jonah and Aki. We’re happy to announce the release of v2.0.0 of the loo R package for efficient approximate leave-one-out cross-validation (and more). For anyone unfamiliar with the package, the original motivation for its development is in our paper: Vehtari, A., Gelman, A., and Gabry, J. (2017). Practical Bayesian model evaluation […]

## Fitting a hierarchical model without losing control

Tim Disher writes: I have been asked to run some regularized regressions on a small N high p situation, which for the primary outcome has lead to more realistic coefficient estimates and better performance on cv (yay!). Rstanarm made this process very easy for me so I am grateful for it. I have now been […]

## Mitzi’s talk on spatial models in Ann Arbor, Thursday 5 April 2018

Mitzi returns to her alma mater to give a talk at joint meeting of the Ann Arbor useR and ASA Meetups: Spatial models in Stan Abstract This case study shows how to efficiently encode and compute an intrinsic conditional autoregressive (ICAR) model in Stan. When data has a neighborhood structure, ICAR models provide spatial smoothing […]

## Combining Bayesian inferences from many fitted models

Renato Frey writes: I’m curious about your opinion on combining multi-model inference techniques with rstanarm: On the one hand, screening all (theoretically meaningful) model specifications and fully reporting them seems to make a lot of sense to me — in line with the idea of transparent reporting, your idea of the multiverse analysis, or akin […]

## Bayesian inference for A/B testing: Lauren Kennedy and I speak at the NYC Women in Machine Learning and Data Science meetup tomorrow (Tues 27 Mar) 7pm

Here it is: Bayesian inference for A/B testing Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University Lauren Kennedy, Columbia Population Research Center, Columbia University Suppose we want to use empirical data to compare two or more decisions or treatment options. Classical statistical methods based on statistical significance and p-values break down […]