This post is by Eric. We have a number of Stan related events in the pipeline. On 22 Nov, Ben Goodrich and I will be holding a free webinar called Introduction to Bayesian Computation Using the rstanarm R Package. Here is the abstract: The goal of the rstanarm package is to make it easier to use Bayesian […]

**Statistical computing**category.

## Recently in the sister blog and elsewhere

Why it can be rational to vote (see also this by Robert Wiblin, “Why the hour you spend voting is the most socially impactful of all”) Be skeptical when polls show the presidential race swinging wildly The polls of the future will be reproducible and open source Testing the role of convergence in language acquisition, […]

## Why I prefer 50% rather than 95% intervals

I prefer 50% to 95% intervals for 3 reasons: 1. Computational stability, 2. More intuitive evaluation (half the 50% intervals should contain the true value), 3. A sense that in aplications it’s best to get a sense of where the parameters and predicted values will be, not to attempt an unrealistic near-certainty. This came up […]

## Michael Betancourt has made NUTS even more awesome and efficient!

In an beautiful new paper, Betancourt writes: The geometric foundations of Hamiltonian Monte Carlo implicitly identify the optimal choice of [tuning] parameters, especially the integration time. I then consider the practical consequences of these principles in both existing algorithms and a new implementation called Exhaustive Hamiltonian Monte Carlo [XMC] before demonstrating the utility of these […]

## Some modeling and computational ideas to look into

Can we implement these in Stan? Marginally specified priors for non-parametric Bayesian estimation (by David Kessler, Peter Hoff, and David Dunson): Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of […]

## “It’s not reproducible if it only runs on your laptop”: Jon Zelner’s tips for a reproducible workflow in R and Stan

Jon Zelner writes: Reproducibility is becoming more and more a part of the conversation when it comes to public health and social science research. . . . But comparatively little has been said about another dimension of the reproducibility crisis, which is the difficulty of re-generating already-complete analyses using the exact same input data. But […]

## Yes, despite what you may have heard, you can easily fit hierarchical mixture models in Stan

There was some confusion on the Stan list that I wanted to clear up, having to do with fitting mixture models. Someone quoted this from John Kruschke’s book, Doing Bayesian Data Analysis: The lack of discrete parameters in Stan means that we cannot do model comparison as a hierarchical model with an indexical parameter at […]

## Practical Bayesian model evaluation in Stan and rstanarm using leave-one-out cross-validation

Our (Aki, Andrew and Jonah) paper Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC was recently published in Statistics and Computing. In the paper we show why it’s better to use LOO instead of WAIC for model evaluation how to compute LOO quickly and reliably using the full posterior sample how Pareto smoothing importance […]

## Mathematica, now with Stan

Vincent Picaud developed a Mathematica interface to Stan: MathematicaStan You can find everything you need to get started by following the link above. If you have questions, comments, or suggestions, please let us know through the Stan user’s group or the GitHub issue tracker. MathematicaStan interfaces to Stan through a CmdStan process. Stan programs are […]

## Tenure Track Professor in Machine Learning, Aalto University, Finland

Posted by Aki. I promise that next time I’ll post something else than a job advertisement, but before that here’s another great opportunity to join Aalto Univeristy where I work, too. “We are looking for a professor to either further strengthen our strong research fields, with keywords including statistical machine learning, probabilistic modelling, Bayesian inference, […]

## “Crimes Against Data”: My talk at Ohio State University this Thurs; “Solving Statistics Problems Using Stan”: My talk at the University of Michigan this Fri

Crimes Against Data Statistics has been described as the science of uncertainty. But, paradoxically, statistical methods are often used to create a sense of certainty where none should exist. The social sciences have been rocked in recent years by highly publicized claims, published in top journals, that were reported as “statistically significant” but are implausible […]

## Let’s play Twister, let’s play Risk

Alex Terenin, Dan Simpson, and David Draper write: Some months ago we shared with you an arxiv draft of our paper, Asynchronous Distributed Gibbs Sampling. Through comments we’ve received, for which we’re highly grateful, we came to understand that (a) our convergence proof was wrong, and (b) we actually have two algorithms, one exact and […]

## Stan users group hits 2000 registrations

Of course, there are bound to be duplicate emails, dead emails, and people who picked up Stan, joined the list, and never came back. But still, that’s a lot of people who’ve expressed interest! It’s been an amazing ride that’s only going to get better as we learn more and continue to improve Stan’s speed […]

## Several postdoc positions in probabilistic modeling and machine learning in Aalto, Helsinki

This post is by Aki In addition to the postdoc position I advertised recently, now Aalto University and University of Helsinki have 20 more open postdoc and research fellow positions. Many of the positions are in probabilistic models and machine learning. You could work with me (I’m also part of HIIT), but I can also […]

## R demos for BDA3

Last year we published some Matlab/Octave and Python demos for BDA3. During the summer my student Markus Paasiniemi ported these demos to R. New R BDA3 demos are now available in github. We hope these are helpful for someone. They are now just R code, although R Markdown would be cool. Btw. we are expecting […]

## How paracompact is that?

Dominic on stan-users writes: I was reading through http://arxiv.org/pdf/1410.5110v1.pdf and came across the term with which I was not familiar: “paracompact.” I wrote a short blog post about it: https://idontgetoutmuch.wordpress.com/2016/04/17/every-manifold-is-paracompact. It may be of interest to other folks reading the aforementioned paper. I would have used a partition of unity to justify the corollary myself […]

## Fast CAR: Two weird tricks for fast conditional autoregressive models in Stan

Max Joseph writes: Conditional autoregressive (CAR) models are popular as prior distributions for spatial random effects with areal spatial data. Historically, MCMC algorithms for CAR models have benefitted from efficient Gibbs sampling via full conditional distributions for the spatial random effects. But, these conditional specifications do not work in Stan, where the joint density needs […]

## Free workshop on Stan for pharmacometrics (Paris, 22 September 2016); preceded by (non-free) three day course on Stan for pharmacometrics

So much for one post a day… Workshop: Stan for Pharmacometrics Day If you are interested in a free day of Stan for pharmacometrics in Paris on 22 September 2016, see the registration page: Stan for Pharmacometrics Day (free workshop) Julie Bertrand (statistical pharmacologist from Paris-Diderot and UCL) has finalized the program: When Who What […]